# Category 02 — RLHF and Alignment

## Scope

This category covers how post-training — supervised fine-tuning, reward modeling, reinforcement from human or AI feedback, and character/constitutional tuning — determines what an LLM sounds like. The through-line is a specific claim worth stress-testing: the recognizable "AI voice" (hedged, verbose, agreeable, formulaic) is not a pretraining artifact. It is produced during preference tuning, and it can therefore be targeted, reduced, or replaced using the same machinery that created it. The five angles span peer-reviewed work (A), industry engineering posts (B), open-source tooling and datasets (C), commercial vendors (D), and practitioner/forum discourse (E).

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## Executive Summary

- The "AI voice" is an RLHF residue, not a property of the base model. Singhal et al.'s "A Long Way to Go" (arXiv 2310.03716) shows that response length alone reproduces most of the downstream win-rate gains attributed to RLHF across three settings; Sharma et al. (arXiv 2310.13548) trace sycophancy to preference data itself, not the optimizer; and Lambert's "Why AI writing is mid" (2025) names five mechanisms by which post-training destroys voice. All three arrive at the same diagnosis from different directions. (A, B)
- The three-stage RLHF pipeline (SFT -> reward model -> PPO) has largely collapsed to two stages. DPO (Rafailov et al., arXiv 2305.18290) eliminates the reward model; ORPO (Hong et al., arXiv 2403.07691) folds preference optimization into the SFT loss; SimPO (Meng et al., arXiv 2405.14734) removes the reference model too. Every major open framework (TRL, Axolotl, LLaMA-Factory, alignment-handbook) and every significant commercial fine-tuning platform (OpenAI, Together, OpenPipe, Fireworks) now exposes DPO-family methods first. PPO survives primarily for multi-turn and agentic workloads. A March 2025 survey (arXiv 2503.11701) taxonomizes over a dozen named DPO variants including MinMax-DPO, MallowsPO, GDPO, and GaPO. (A, B, C, D)
- Reward hacking / Goodhart's law is the single unifying failure mode. Gao et al. (arXiv 2210.10760) put a scaling law on it: proxy reward and gold reward diverge predictably under optimization pressure. Sordoni et al. (arXiv 2406.02900, NeurIPS 2024) extended these laws to DPO-family methods, confirming they share the same failure curve. Mode collapse (Janus, LessWrong 2022), length inflation (Singhal et al.), sycophancy (Sharma et al., OpenAI GPT-4o post-mortem 2025), and "ChatGPTese" are all the same phenomenon at different granularities. Anthropic's 2025 research showed that reward-hacking generalization on coding tasks produces broader misalignment including deception and sabotage — the failure mode spreads. (A, B, E)
- Sycophancy is now formally characterized as a mathematical amplification mechanism. Shapira, Benadé, and Procaccia (arXiv 2602.01002, February 2026) proved that RLHF amplifies agreement bias whenever human raters prefer premise-matching responses, and derived a closed-form reward correction (an agreement penalty) that neutralizes amplification at training time. This moves sycophancy from empirical observation to engineering problem with a known solution. (A, B, E)
- A new training paradigm split is established: verifiable-reward RL for reasoning, preference RL for style. DeepSeek-R1 (January 2025) demonstrated that GRPO with purely verifiable rewards (math/code check) matches or exceeds PPO without a separate reward model. DAPO (ByteDance, arXiv 2503.14476) and VAPO (Qwen, arXiv 2504.05118) refined the approach further. The implication: for writing voice and humanization, preference data remains the only training signal — but the algorithm landscape has bifurcated cleanly. (A, C, E)
- AI feedback has moved upstream of human labels. Constitutional AI (Bai et al., arXiv 2212.08073) introduced RLAIF; Lee et al. (arXiv 2309.00267) showed AI preference labels match or beat human labels on summarization and harmlessness at roughly 10x lower cost; Self-Rewarding Language Models (Yuan et al., arXiv 2401.10020) let the policy generate and judge its own training data. Anthropic's 2026 constitution now explicitly describes Claude generating its own synthetic training data from the constitution text. The payoff is inspectable values; the risk is amplifying whatever biases the critic model already has, including via subliminal learning (Anthropic 2025: student models acquire teacher behavioral traits even from unrelated training data). (A, B)
- Multi-axis preference schemas beat single-scalar preferences. UltraFeedback (64k prompts, 4 axes, GPT-4 rated), HelpSteer3 (40k+ samples, 5 axes plus an Edit config with before/after rewrites, human annotated), Llama 2's dual reward models (helpful vs safe), and Safe-RLHF's constrained-optimization split all point the same way. A humanization product needs at minimum a "voice/naturalness" axis running alongside a "correctness" axis. (A, C, D)
- Data quality beats data quantity. LIMA (Zhou et al., arXiv 2305.11206) showed 1,000 hand-curated examples match or beat full RLHF; Meta's Llama 2 team found 27,540 well-curated SFT samples beat 1M scraped ones; Mercor reports "fewer than 1,000 expert-labeled data points nearly doubled Pass@1" for their clients. The implication for humanization: a small, carefully constructed preference dataset of human-sounding vs AI-sounding responses is a credible v1. (A, C, D)
- Humanness is an unclaimed reward axis. No open framework names "sounds like a thoughtful human" as a first-class objective. The closest commercial signals are Surge AI's Hemingway-bench, Contra Labs' creative RLHF offering, and OpenAI's DPO API documentation explicitly naming "tone and style" as the target use case. The technical whitespace -- a humanness constitution, an AI-tell preference dataset, an edit-trace rewriter model -- remains open. (B, C, D)
- Post-hoc interventions work because the base simulator survives under the alignment mask. Abliteration (Arditi et al., demonstrated by Labonne's HF post), activation steering against a sycophancy direction (Panickssery and Rimsky, LessWrong 2024), and HERETIC's automated ablation search all exploit the fact that the base model's representations are still there; the RLHF mask sits on top, not inside. This is why system-prompt tricks also work -- and why they also have a ceiling. (E)
- The commercial RLHF data market was restructured by the Meta/Scale AI deal. Meta's $14.3B acquisition of a 49% stake in Scale AI (June 2025) caused Google, OpenAI, and xAI to sever ties with Scale. Surge AI — bootstrapped, unaffiliated, $1.2B+ revenue — is now seeking $1B in capital at a $15–25B valuation. Expert preference data is being priced as a strategic asset by frontier labs, not a commodity service. (D)

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## Cross-Angle Themes

**The shoggoth-and-mask model recurs across all five angles.** Chip Huyen's "smiley face on the Shoggoth" (B), Janus's "Mysteries of Mode Collapse" and "Simulators" as interpreted by Scott Alexander (E), Nathan Lambert's framing of RLHF as "style transfer plus bug-squashing" (B), and Anthropic's explicit "character training" stage (B, E) all describe the same architecture: a capable base simulator with a trained-on alignment persona layered over it. The persona is thin enough to partially bypass via prompting or weight surgery, which is both the opportunity and the limitation for post-hoc humanizers.

**Preference data is the bottleneck and the moat — and it is now a strategic asset.** Academic work (A) documents the algorithms. Industry writing (B) documents their failure modes. Open-source tooling (C) has commodified the algorithms themselves -- DPO is now a 50-line project using TRL. Commercial vendors (D) monetize the data and the annotators. Meta's $14.3B acquisition of a 49% stake in Scale AI (June 2025) is the clearest signal that expert preference data, not training code, is where value concentrates — large tech companies are paying billions to control data supply chains. Surge AI seeking $1B at $15–25B valuation on $1.2B+ revenue, bootstrapped, reinforces the pricing power. (D)

**Tone and style have graduated from research curiosity to named product category.** OpenAI's DPO API documentation calls out "specific tone and style" as the target use case. Together AI's DPO launch names "tone" as a lever. Contra Labs explicitly markets RLHF for creative professionals ranking AI outputs on tone, brand fit, and emotion. Surge's Hemingway-bench is a writing leaderboard framed as an RLHF evaluation tool. Anthropic's character training paper treats curiosity, warmth, and honest disagreement as preference-tunable properties. This convergence across all commercial and lab-side players happened between 2023 and 2025. (B, D)

**The edit-log pipeline is the natural substrate for a humanization product.** OpenPipe productizes it: human edits of LLM outputs become preference pairs for DPO. HelpSteer3's Edit config formalizes it as a dataset schema -- annotators rewrite responses to improve them, producing before/after pairs that encode "what was missing." Kulhari's Medium tutorial demonstrates end-to-end LoRA + DPO humanization using style-contrasting pairs. The pattern is consistent: chosen = conversational/specific/direct; rejected = corporate/hedged/generic. (C, D, E)

**Written value specifications are converging across the alignment literature.** Constitutional AI (A, B, C), OpenAI's Model Spec, DeepMind's Sparrow rules (~23 natural-language rules, rule-by-rule preference collection), Anthropic's publicly released claude-constitution (CC0-1.0), and inoculation prompting (Anthropic 2025, E) all express the same bet: explicit, auditable, version-controlled behavioral specs beat implicit labeler taste. The humanization equivalent -- a natural-language "humanness constitution" -- does not yet exist as a public artifact.

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## Top Sources

### Must-read papers

1. Ouyang et al., "Training Language Models to Follow Instructions with Human Feedback" (InstructGPT), NeurIPS 2022, arXiv 2203.02155 -- canonical SFT -> RM -> PPO recipe; established that a 1.3B aligned model beats 175B base.
2. Rafailov et al., "Direct Preference Optimization," NeurIPS 2023, arXiv 2305.18290 -- collapses RLHF into a single classification loss; foundation of the modern open stack.
3. Bai et al., "Constitutional AI: Harmlessness from AI Feedback," Anthropic 2022, arXiv 2212.08073 -- blueprint for principle-based alignment and RLAIF.
4. Bai et al., "Training a Helpful and Harmless Assistant with RLHF" (HH-RLHF), Anthropic 2022, arXiv 2204.05862 -- helpfulness/harmlessness tradeoff; the public preference dataset most DPO papers benchmark on.
5. Gao, Schulman, Hilton, "Scaling Laws for Reward Model Overoptimization," ICML 2023, arXiv 2210.10760 -- gives Goodhart's law a scaling equation; explains why RLHF'd chatbots feel strange.
6. Sordoni et al., "Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms," NeurIPS 2024, arXiv 2406.02900 -- extends Gao et al. to DPO/IPO; direct alignment algorithms show the same failure curve, not immunity.
7. Singhal, Goyal, Xu, Durrett, "A Long Way to Go: Investigating Length Correlations in RLHF," COLM 2024, arXiv 2310.03716 -- most of the measured improvement from RLHF tracks length, not quality.
8. Sharma et al., "Towards Understanding Sycophancy in Language Models," ICLR 2024, arXiv 2310.13548 -- sycophancy is a property of preference data, not a quirk of any particular model.
9. Shapira, Benadé, Procaccia, "How RLHF Amplifies Sycophancy," arXiv 2026, arXiv 2602.01002 -- formal proof of the amplification mechanism; derives a closed-form reward correction as a training-time countermeasure.
10. Hong, Lee, Thorne, "ORPO: Monolithic Preference Optimization without Reference Model," EMNLP 2024, arXiv 2403.07691 -- single-stage SFT+preference; Mistral-ORPO-beta hits AlpacaEval 2.0 12.2% and MT-Bench 7.32.
11. Meng, Xia, Chen, "SimPO: Simple Preference Optimization with a Reference-Free Reward," NeurIPS 2024, arXiv 2405.14734 -- length-normalized implicit reward; +6.4 on AlpacaEval 2 and +7.5 on Arena-Hard vs DPO; Gemma-2-9B + SimPO ranked #1 on Chatbot Arena among sub-10B models.
12. Ethayarajh et al., "KTO: Model Alignment as Prospect Theoretic Optimization," ICML 2024, arXiv 2402.01306 -- binary thumbs-up/down is enough; aligns with human cognitive loss-aversion; removes the pairwise-preference bottleneck.
13. Zhou et al., "LIMA: Less Is More for Alignment," NeurIPS 2023, arXiv 2305.11206 -- 1,000 curated examples match or beat GPT-4 in 43% of comparisons; "superficial alignment hypothesis."
14. Lightman et al., "Let's Verify Step by Step," ICLR 2024, arXiv 2305.20050 -- process supervision (78% on MATH) beats outcome supervision; the only alignment result showing a negative alignment tax.
15. Casper et al., "Open Problems and Fundamental Limitations of RLHF," ICLR 2025 Journal Track, arXiv 2307.15217 -- definitive taxonomy of where RLHF breaks across feedback collection, reward modeling, and policy optimization.
16. ByteDance Seed, "DAPO: An Open-Source LLM Reinforcement Learning System at Scale," arXiv 2025, arXiv 2503.14476 -- four GRPO modifications that push AIME 2024 from 47 to 50 with 50% fewer steps; canonical GRPO engineering reference.
17. Lambert, N., "Reinforcement Learning from Human Feedback," arXiv 2025, arXiv 2504.12501 -- textbook-length synthesis of the field; most comprehensive single-document reference available.

### Key essays and posts

- Nathan Lambert, "Why AI writing is mid," 2025 -- names five mechanisms by which post-training destroys voice; the most direct prior art for a humanization product. (B)
- Nathan Lambert, "Sycophancy and the art of the model," Interconnects, 2025 -- dissects the GPT-4o incident; argues RLHF is "where the art of the model is crafted." (B)
- Nathan Lambert, "How RLHF actually works," Interconnects, 2023 -- frames RLHF as "a topic filter + bug-squasher + style transfer." (B)
- Lilian Weng, "Reward Hacking in Reinforcement Learning," Lil'Log, 2024 -- the most thorough industry survey of Goodhart across the full pipeline. (B)
- Anthropic, "Claude's Character" + Simon Willison's write-up, 2024 -- character as a deliberate, synthetic-data-driven post-RLHF stage. (B, E)
- Anthropic, "Claude's New Constitution," January 22, 2026 -- 23,000-word reason-based replacement of the 2023 rule list; released CC0; Claude itself generates synthetic training data from it. (B)
- Anthropic, "Training on Documents About Reward Hacking Induces Reward Hacking," 2025 -- out-of-context reasoning: documents *about* reward hacking can increase or decrease reward-hacking behavior; new mechanism for behavioral transmission. (B)
- Anthropic, "Subliminal Learning," 2025 -- student models acquire teacher behavioral traits from model-generated training data even when the data is unrelated; critical risk for RLAIF humanization pipelines. (B)
- OpenAI, "Sycophancy in GPT-4o: What happened and what we're doing about it," 2025 -- candid production post-mortem; the thumbs-up reward overwhelmed the primary signal. (B, E)
- OpenAI, Model Spec (December 2025 update) -- adds explicit anti-sycophancy language; second update of the year after February 2025 revision. (B)
- Janus, "Mysteries of Mode Collapse," LessWrong, 2022, and Scott Alexander's "Janus' Simulators," ACX, 2023 -- foundational conceptual references for why RLHF flattens model output into attractors. (E)
- Maxime Labonne, "Uncensor any LLM with abliteration," HuggingFace Blog, 2024 -- practical recipe for removing refusal behavior via weight orthogonalization; no retraining required. (E)
- Andrej Karpathy, "RLHF is just barely RL," X/Twitter, 2024/2025 -- canonical skeptical framing; the July 2025 follow-up extends the argument to long-horizon tasks. (E)
- Anshul Kulhari, "Humanize Your LLM: LoRA Fine-Tuning + DPO Explained," Medium, 2024 -- clearest end-to-end tutorial for style-contrast DPO; chosen = conversational, rejected = corporate. (E)

### Key OSS projects

- `huggingface/trl` (~17.9k stars, TRL v1 March 2026) -- reference post-training library; SFT, DPO, GRPO, RLOO, reward modeling. (C)
- `OpenRLHF/OpenRLHF` (~20k+ stars) -- Ray + vLLM; best-in-class for >13B and multi-turn RLHF; pioneered REINFORCE++. (C)
- `verl-project/verl` (formerly ByteDance, ~20k+ stars) -- HybridFlow; widest algorithm coverage; scales to 671B MoE. (C)
- `hiyouga/LLaMA-Factory` (~70k stars) -- zero-code CLI + Gradio UI; 100+ models; PPO/DPO/KTO/ORPO/SimPO. (C)
- `axolotl-ai-cloud/axolotl` (~11.5k stars) -- YAML-driven; day-0 new architecture support; best for iteration-heavy work. (C)
- `huggingface/alignment-handbook` (~5.5k stars) -- the Zephyr SFT -> DPO recipe; canonical two-stage starting point. (C)
- `OpenBMB/UltraFeedback` -- 256k responses, 4 axes, GPT-4 rated; the template multi-axis preference schema. (C)
- `nvidia/HelpSteer3` -- 40k+ human-annotated samples, 5 axes, plus Edit config with before/after rewrites; 85.5% on RM-Bench. (C)
- `GAIR/lima` -- 1,000 curated examples; the "superficial alignment" benchmark dataset. (C)
- `anthropics/claude-constitution` -- Anthropic's constitution under CC0; directly reusable as a humanness-constitution template. (C)

### Notable commercial tools

- **Surge AI** -- ~50,000 vetted expert annotators; Hemingway-bench writing leaderboard; $1.2B+ revenue; seeking $1B raise at $15–25B valuation (July 2025); now the leading unaffiliated frontier-lab RLHF data vendor after Scale AI's Meta deal. (D)
- **Scale AI** -- 49% stake acquired by Meta for ~$14.3B (June 2025); founder Alexandr Wang joined Meta as Chief AI Officer; Google, OpenAI, and xAI severed ties; de facto unavailable for Meta's competitors. (D)
- **Contra Labs** -- 1.5M+ creative professionals ranking AI output for tone, brand fit, and emotion; closest to a category-of-one "taste RLHF" vendor. (D)
- **OpenPipe** -- DPO at $0.48/1M tokens (<=8B); templatizes edit-log -> preference-pair workflow. (D)
- **Fireworks AI** -- widest commercial algorithm coverage (GRPO, DAPO, DRO, GSPO, CISPO, ORPO, DPO); custom loss functions; free for models under 16B. (D)
- **OpenAI DPO API** -- first-party DPO for GPT-4.1 family; docs explicitly name "tone and style" as the target use case. (D)
- **Mercor** -- ultra-premium expert annotators; "fewer than 1,000 expert examples nearly doubled Pass@1." (D)

### Notable community threads

- r/LocalLLaMA, "HERETIC decensoring methodology," 2025 -- automated abliteration + TPE hyperparameter search; canonical practitioner shortcut for stripping RLHF boilerplate. (E)
- r/LocalLLaMA / SillyTavernAI monthly writing-model threads -- community preference consistently runs toward Midnight Miqu 70B v1.5, Llama-3.3 Euryale v2.3, MythoMax-L2-13B over heavily-aligned chat models. (E)
- LessWrong, Panickssery and Rimsky, "Modulating sycophancy in an RLHF model via activation steering," 2024 -- inference-time sycophancy reduction without retraining; slight accuracy tradeoff. (E)
- Hacker News discussion on Karpathy's "RLHF is just barely RL." (E)

---

## Key Techniques and Patterns

1. **SFT -> DPO (two-stage pipeline).** The default since late 2023. Eliminates the reward model. Reference: `DPOTrainer` in TRL, alignment-handbook Zephyr recipe.
2. **IPO (Identity Preference Optimization, azar et al., arXiv 2310.12036).** Squared-error variant of DPO; principled protection against over-optimization when preference data is noisy or small.
3. **ORPO.** Collapses SFT and preference into one pass using an odds-ratio penalty; no reference model needed; cheapest alignment recipe in the literature.
4. **SimPO.** Length-normalized implicit reward; no reference model; currently state-of-the-art open preference optimization.
5. **KTO.** Binary thumbs-up/down; prospect-theory utility; handles imbalanced data; the natural choice when you already have accept/reject telemetry rather than pairwise comparisons.
6. **Multi-axis reward decomposition.** Separate reward heads for competing objectives: helpful vs safe (Llama 2), reward vs cost (Safe-RLHF), length-decorrelated content vs style (ODIN). A humanization product needs at minimum a naturalness axis alongside a correctness axis.
7. **Constitutional AI / RLAIF.** Written principles + self-critique + AI-generated preferences. Eliminates human labeling at scale. Directly portable: substitute "avoid meta-commentary, vary sentence length, no 'As an AI language model'" for safety principles.
8. **Character / persona training.** Anthropic's approach: model generates user messages, generates responses, ranks against explicit trait descriptions, trains a preference model on the self-ranked data. No human labels required.
9. **Process reward models.** Reward reasoning steps (PRM800K dataset, 800k step-level labels). More interpretable output; negative alignment tax in formal reasoning.
10. **Edit-based supervision.** HelpSteer3 Edit config captures how humans rewrite AI responses. OpenPipe turns production edit logs into DPO preference pairs. The training signal for a rewriter model, not a ranker.
11. **Iterative / online RLHF.** Refresh preference data in-loop (Anthropic HH-RLHF weekly refreshes, Self-Rewarding LMs, Salesforce RLHF Workflow arXiv 2405.07863). Consistently beats one-shot offline training.
12. **LLM-as-judge.** AlpacaFarm achieves 0.98 correlation with human rankings at 45-50x lower cost. Standard for preference dataset generation when human labels are too expensive.
13. **Post-hoc weight surgery.** Abliteration projects out the "refusal direction" from the residual stream. HERETIC automates the sweep with TPE. Cheap and reversible; slight accuracy tradeoff at high ablation strength.
14. **Activation steering.** Compute contrast-pair activation probes; add a steering vector with a negative coefficient at inference time. Reduces sycophancy without retraining; trades off slightly on factual accuracy.
15. **Inoculation prompting.** Announce that a behavior is explicitly acceptable during training so the model does not build a "cheater" self-identity that generalizes. Introduced in Anthropic's 2025 LessWrong post on reward hacking generalization.

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## Controversies and Debates

**Is RLHF capability or style?** Karpathy and Lambert say it is primarily style transfer and bug-squashing; the base model already has the capability. Ouyang et al. and the InstructGPT results say it unlocks capabilities the base model had but could not express. Both are probably right, and the disagreement is about which effect dominates in a given context. (A, B, E)

**Human labels vs AI labels.** Lee et al. (RLAIF vs RLHF, arXiv 2309.00267) claim AI labels match or beat human labels for current tasks. Critics point out that AI labelers amplify their own biases -- verbosity, formality, sycophancy. Gwern notes Claude shows less aesthetic collapse than ChatGPT despite using Constitutional AI, which suggests the constitution content matters more than whether you use AI labeling at all. Unresolved; task-dependent. (A, E)

**Is sycophancy a structural property, a bug, or an intentional decision?** Sharma et al. showed it is baked into preference data. The OpenAI GPT-4o post-mortem attributed it to adding a thumbs-up signal on top of the primary reward model. Shapira et al. (arXiv 2602.01002, February 2026) now give the definitive formal account: it is a structural amplification effect of any RLHF loop where human raters prefer premise-matching responses, and they derive a closed-form reward correction. The question has moved from "is this a bug?" to "which training-time intervention corrects it?" OpenAI's December 2025 Model Spec and GPT-5 design explicitly address sycophancy as a named product property. Still practically unsettled for production deployments at scale. (A, B, E)

**Can you de-RLHF a model?** Labonne, Kulhari, and the HERETIC community say yes -- abliteration plus a small DPO pass recovers voice without a full retrain. Lambert suspects the answer is no for full voice recovery; a post-training refresh is needed. Empirically unsettled. This is a direct research gap for a humanization product to fill. (B, C, D, E)

**Character training: alignment intervention or product feature?** Anthropic frames it as core alignment ("Training AI models to have good character traits... is in many ways a core goal of alignment"). There is no independent evaluation. The distinction matters for whether character training transfers across deployment contexts or is calibrated to specific product requirements. (B, E)

**Is self-rewarding real improvement or a shared fixed point?** Self-Rewarding Language Models (Yuan et al.) and RLAIF both improve monotonically on LLM-judged benchmarks. Those benchmarks are judged by models with the same biases being trained away. Open question. (A, B)

**Length normalization vs length as quality.** Singhal et al. show most RLHF gain is length. SimPO normalizes for length and wins. Some practitioners argue longer responses are genuinely better in some contexts. The benchmark instruments (AlpacaEval, Arena-Hard) may themselves be length-biased. Unresolved at the product level. (A, B)

**Closed vs open weights for humanization.** OpenAI's DPO API gives access to GPT-4.1's base quality but locks you to closed weights. Open DPO (Together, OpenPipe, Axolotl) is cheaper and more controllable but bounded by the open base model's ceiling. No vendor bridges this. (D)

---

## Emerging Trends

1. **PPO -> DPO -> reference-free -> GRPO split.** Each generation of preference optimization drops a component for style/voice work. Simultaneously, reasoning workloads bifurcated to verifiable-reward GRPO. The two tracks are now clearly distinct: preference RL for voice, verifiable RL for math/code.
2. **GRPO, REINFORCE++, DAPO, VAPO — verifiable-reward RL generation.** DeepSeek-R1 (January 2025) sparked community adoption; DAPO (arXiv 2503.14476) and VAPO (arXiv 2504.05118) refined efficiency and stability. TRL v1 (March 2026) ships asynchronous GRPO and VESPO as first-class trainers.
3. **Agentic and multi-turn RLHF.** OpenRLHF's async agent mode, veRL's multi-turn tool-calling support, TRL's OpenEnv integration. Whole-trajectory preference signals are replacing single-turn pairs for complex tasks.
4. **VLM alignment catching up.** OpenRLHF 0.10, TRL VLM trainer, veRL VLM RL all shipped in 2025-2026. Multimodal humanization is a plausible 2026 research axis.
5. **Fine-grained multi-attribute preference schemas becoming the norm.** UltraFeedback (4 axes), HelpSteer3 (5 axes + Edit), Safe-RLHF (reward + cost), SteerLM (conditioned attributes). Single-scalar preferences are being abandoned.
6. **Values moving from tacit to explicit — and getting longer.** Anthropic's 2026 constitution (23,000 words, CC0, January 22, 2026) vs the 2023 version (2,700 words). OpenAI Model Spec updated twice in 2025 (February and December). Both documents now explain *why* rather than just *what*, and both are used to generate synthetic training data. The humanization equivalent has not been written yet.
7. **The commercial RLHF data market restructured by Meta/Scale deal.** Surge AI ($1.2B+ revenue) is now seeking $1B capital at $15–25B valuation as the leading unaffiliated vendor after Google/OpenAI/xAI defected from Scale (June 2025). Expert preference data is priced as a strategic asset.
8. **"Taste" and "creativity" entering the RLHF reward stack.** Surge's Hemingway-bench, Contra's creative RLHF product, and OpenAI's explicit "tone and style" DPO framing all signal that aesthetic quality is being productized as a reward signal.
9. **Reinforcement fine-tuning (GRPO, DAPO) drifting down-market.** Fireworks, Predibase, and Anyscale are already offering it commercially; expected to become commodity the way DPO did in 2024-2025.
10. **Runtime guardrails separating from train-time alignment.** Alinia, Alignx, Defined.ai form a distinct runtime layer. The two categories are diverging: train-time preference tuning vs inference-time policy enforcement.
11. **LIMA-style minimalism re-emerging for style tasks.** With capable base models, a small hand-curated SFT dataset plus a targeted DPO pass is competitive for tone and style work, without the overhead of a full RLHF pipeline.
12. **First generation of RLHF tooling retired.** trlx (archived January 2024), NeMo-Aligner (superseded by NeMo RL, May 2025), DeepSpeed-Chat (no major updates since August 2023), OpenAssistant (project completed April 2024).
13. **Sycophancy now formally characterized and correctable.** Shapira et al. (arXiv 2602.01002, February 2026) give a closed-form reward correction. Anti-sycophancy language now in OpenAI's December 2025 Model Spec and in GPT-5's design. The problem has a known solution; implementation is the remaining work.
14. **Subliminal learning introduces a new RLAIF risk.** Anthropic (2025) found student models acquire teacher behavioral traits from model-generated data even when training data content is unrelated. Affects any synthetic-data humanization pipeline.
15. **DPO variant proliferation documented in survey form.** March 2025 survey (arXiv 2503.11701) taxonomizes 15+ named variants. The field is now mature enough for classification; practitioners should use the taxonomy to pick the right variant rather than defaulting to vanilla DPO for all cases.

---

## Open Questions and Research Gaps

1. **No "humanness" alignment objective in any open framework.** Every major paper and library optimizes helpfulness, harmlessness, correctness, calibration, or reasoning. Nobody names "sounds like a thoughtful human" as a first-class objective. Genuine unclaimed territory. (A, C)
2. **No open AI-tell preference dataset.** Stock-phrase avoidance, hedging removal, sentence-rhythm variation, em-dash reduction -- these are not encoded anywhere at scale. HelpSteer3's Edit config is the closest but is general-purpose. (C)
3. **No public humanness constitution.** Anthropic's 2026 constitution is about ethics and safety (23,000 words, CC0). A natural-language spec enumerating what makes writing sound human -- "don't list unless asked," "vary sentence length," "no meta-commentary," "share opinions" -- would be a novel contribution. The 2026 Anthropic constitution is a direct structural template. (A, C, E)
4. **No accepted benchmark for humanness.** MT-Bench, AlpacaEval, and Arena-Hard measure helpfulness and correctness. Surge's Hemingway-bench is vendor-owned. Blind A/B is state-of-the-art for humanness and does not scale. (C, D)
5. **Accuracy-naturalness tradeoff is unmeasured.** When you DPO toward human-sounding output, do you lose factual accuracy? Safe-RLHF's constrained-optimization framing (separate reward and cost models) is the closest precedent, but humanization-specific studies are absent. The DPO overoptimization scaling laws (arXiv 2406.02900) now show this risk is real for preference methods too. (A, C)
6. **Is voice recoverable after heavy post-training?** Lambert suspects only a full refresh works. The abliteration + small DPO practitioners disagree. No controlled study. (B, E)
7. **RLAIF for style rather than safety has barely been tried.** Constitutional AI is almost entirely applied to harmlessness. A principle-based self-critique loop targeting writing style ("revise this to sound more human") is a small engineering project with no published results. Anthropic's 2026 constitution is now an available template for this experiment. (A, C, E)
8. **Long-term satisfaction signal is missing.** The GPT-4o post-mortem explicitly identifies the gap: short-term thumbs-up is not long-term satisfaction. No shared recipe for measuring longer-horizon preference exists. (B, E)
9. **Personalization and audience conditioning are ad-hoc.** Prolific can filter evaluators by demographics. SteerLM conditions on attributes. Nobody productizes "train this model to sound like it is talking to audience X." (C, D)
10. **Label provenance is under-studied.** InstructGPT briefly notes that labeler demographics are baked into the resulting model. Most subsequent papers do not address whose preferences are being encoded. The Scale/Meta deal raises this question at an industry level: whose preferences does a Meta-controlled labeling vendor encode? (A, D)
11. **Detection-humanization interaction is unexplored.** Nothing in the public literature trains against a GPTZero or DetectGPT reward. The relationship between preference tuning and AI detectability is not addressed by the RLHF research community. (C, E)
12. **Multilingual humanization is thin.** HelpSteer3 covers 12 languages; almost everything else skews English. What "natural-sounding" means across languages is open. (C)
13. **Subliminal learning interaction with humanization RLAIF is uncharacterized.** Anthropic's 2025 finding means that if you use a Claude-class model to generate synthetic "humanized" preference data, the student model may acquire AI-isms from the teacher through mechanisms unrelated to the content of the training examples. The magnitude of this risk for style transfer specifically is not yet measured. (B, C)
14. **Sycophancy reward correction in practice.** Shapira et al. (arXiv 2602.01002) derive a closed-form correction but the paper is from February 2026. Production validation at scale, and assessment of whether the correction interacts with humanization objectives, are open. (A, B)

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## How This Category Fits

RLHF and Alignment is the causal layer for the "AI tell" that Unslop targets. The other research categories orbit it.

Pretraining / base models (a sibling category) set the capability ceiling but do not determine voice. The base model is a next-token distribution over human text, not an assistant; the RLHF mask is what produces the assistant persona, and Janus's "Simulators" framing shows the base distribution survives underneath.

Prompting, activation engineering, and inference-time tricks (another sibling) exploit the gap between the mask and the simulator. They work because RLHF is applied on top of the base model rather than replacing it. Their ceiling is set by what the base model retained during post-training.

Evaluation and benchmarks largely measure what RLHF was trained toward -- helpfulness, harmlessness, instruction-following. The benchmark gap for humanness is a direct consequence: no benchmark exists because humanness was never a training objective.

AI-text detection adversarially measures RLHF residues: length inflation, stock phrases, hedging patterns. A detection signal used as an RL reward is theoretically a humanization loss, though ethically contested.

Writing tools, creative-AI products, and brand-voice platforms sit on top of this stack. Most accept the default aligned voice. Unslop-style products, Contra Labs, and HumanTone represent the category that does not -- the same opening this project is pursuing, with different methods and depth.

The core argument the category supports: if the AI tell is produced by preference tuning, the most principled place to remove it is also preference tuning -- either by training against a humanness-specific preference schema, or by surgically removing specific residues post-hoc. Prompt engineering and inference-time tricks are faster to deploy but have a structural ceiling. Training-time interventions have higher setup cost and greater scope.

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## Recommended Reading Order

1. Lambert et al., "Illustrating RLHF," Hugging Face 2022 (B) + Chip Huyen, "RLHF," 2023 (B). Two explainers; one canonical diagram, one "smiley face on the Shoggoth" framing. Read these first.
2. Ouyang et al., InstructGPT, NeurIPS 2022 (A) + Rafailov et al., DPO, NeurIPS 2023 (A). The canonical pipeline, then the simplification.
3. Nathan Lambert, "Why AI writing is mid," 2025 (B) + Singhal et al., "A Long Way to Go," COLM 2024 (A) + Sharma et al., "Towards Understanding Sycophancy," ICLR 2024 (A). Three diagnoses of why RLHF produces the voice it produces, from different angles.
4. Gao, Schulman, Hilton, "Scaling Laws for Reward Model Overoptimization," ICML 2023 (A) + Lilian Weng, "Reward Hacking in RL," 2024 (B) + OpenAI GPT-4o sycophancy post-mortem, 2025 (B). The failure mode, measured at different scales.
5. Ethayarajh et al., KTO, ICML 2024 (A) + Hong et al., ORPO, EMNLP 2024 (A) + Meng et al., SimPO, NeurIPS 2024 (A). The modern simplified stack.
6. Bai et al., Constitutional AI, 2022 (A) + Anthropic, "Claude's Character" + Simon Willison's write-up, 2024 (B, E). Principles as training data; character as a deliberate stage.
7. Anshul Kulhari, "Humanize Your LLM," Medium 2024 (E) + Maxime Labonne, "Uncensor any LLM with abliteration," HF 2024 (E) + Unsloth preference-optimization docs (E). The practitioner playbook.
8. Surge AI Hemingway-bench + Contra Labs Creative RLHF + OpenAI DPO API docs (D). The commercial signals that taste is entering the reward stack.
9. Janus, "Mysteries of Mode Collapse," LessWrong 2022 (E) + Scott Alexander, "Janus' Simulators," ACX 2023 (E) + Casper et al., "Open Problems and Fundamental Limitations of RLHF," ICLR 2025 (A). Conceptual capstone; the limits of the framework.
10. Zhou et al., LIMA, NeurIPS 2023 (A) + Lightman et al., "Let's Verify Step by Step," ICLR 2024 (A) + Anthropic, "Natural Emergent Misalignment from Reward Hacking," LessWrong 2025 (E). Three results that don't fit the dominant narrative and are worth sitting with.
