Pith. sign in

REVIEW 14 cited by

Reward-Robust RLHF in LLMs

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.15360 v3 pith:KN4XLYFF submitted 2024-09-18 cs.LG cs.AIcs.CL

Yuzi Yan , Xingzhou Lou , Jialian Li , Yiping Zhang , Jian Xie , Chao Yu , Yu Wang , Dong Yan
show 1 more author
Yuan Shen
This is my paper
classification cs.LGcs.AIcs.CL
keywords rewardframeworkrlhflearningllmsperformancereward-robuststability
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pathway toward achieving Artificial General Intelligence (AGI). However, the reliance on reward-model-based (RM-based) alignment methods introduces significant challenges due to the inherent instability and imperfections of Reward Models (RMs), which can lead to critical issues such as reward hacking and misalignment with human intentions. In this paper, we introduce a reward-robust RLHF framework aimed at addressing these fundamental challenges, paving the way for more reliable and resilient learning in LLMs. Our approach introduces a novel optimization objective that carefully balances performance and robustness by incorporating Bayesian Reward Model Ensembles (BRME) to model the uncertainty set of reward functions. This allows the framework to integrate both nominal performance and minimum reward signals, ensuring more stable learning even with imperfect RMs. Empirical results demonstrate that our framework consistently outperforms baselines across diverse benchmarks, showing improved accuracy and long-term stability. We also provide a theoretical analysis, demonstrating that reward-robust RLHF approaches the stability of constant reward settings, which proves to be acceptable even in a stochastic-case analysis. Together, these contributions highlight the framework potential to enhance both the performance and stability of LLM alignment.

Discussion (0). Sign in to comment.

Forward citations

Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Efficient Preference Poisoning Attack on Offline RLHF

    cs.LG 2026-05 unverdicted novelty 8.0 of 10

    Label-flip attacks on log-linear DPO reduce to binary sparse approximation problems that can be solved efficiently by lattice-based and binary matching pursuit methods with recovery guarantees.

  2. The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning

    cs.LG 2026-06 unverdicted novelty 7.0 of 10

    PRISM is a contrastive, policy-aware training framework for process reward models that reduces false positives by 22% on PRMBench and boosts downstream accuracy up to 33% in Best-of-N selection by learning reliable re...

  3. Efficient Preference Poisoning Attack on Offline RLHF

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    Preference poisoning against log-linear DPO reduces to a binary sparse approximation problem solved by lattice-reduction (BAL-A) and matching-pursuit (BMP-A) algorithms that carry recovery guarantees.

  4. Multimodal Reward Hacking in Reinforcement Learning

    cs.AI 2026-07 conditional novelty 6.5 of 10

    Imperfect multimodal RL rewards systematically create new failures (NRFR > RHR); scaling and answer-aware rewards help but do not eliminate hacking, and unreliable visual verifiers actively increase it.

  5. Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

    cs.LG 2026-04 conditional novelty 6.5 of 10

    Wasserstein distributionally robust regret optimization for RLHF admits a promptwise water-filling solution and a GRPO-compatible sampled bonus that mitigates over-optimization better than value-robust DRO.

  6. A Unifying Lens on Reward Uncertainty in RLHF

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    A distributional reward model p(r|x,y) yields the closed-form effective reward ilde r(x,y) = eta ext{log} ext{E}_p[e^{r/eta}] (pessimistic branch) that unifies prior RLHF aggregation heuristics under Bayesian or ...

  7. SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

    cs.AI 2026-05 conditional novelty 6.0 of 10

    A 24-dataset benchmark for inducing schema graphs from raw text, plus an auditable LLM-based pipeline that reports the highest scores on the benchmark's four schema-similarity metrics.

  8. PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs

    cs.AI 2026-05 unverdicted novelty 6.0 of 10

    PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.

  9. Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    DRRO for RLHF minimizes worst-case regret relative to the best policy under Wasserstein reward perturbations, yielding an exact inner solution and water-filling policy structure for the promptwise simplex model plus a...

  10. Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    DRRO for RLHF replaces worst-case value with worst-case regret in Wasserstein DRO, producing an exact water-filling solution under l1 ambiguity and a practical sampled-bonus algorithm that reduces proxy over-optimization.

  11. Learning to Control Summaries with Score Ranking

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    A score-ranking loss enables controllable summarization by aligning outputs to evaluation scores, matching SOTA performance with dimension-specific control on LLaMA, Qwen, and Mistral.

  12. Quantifying Empirical Compute-Supervision Tradeoffs in RLVR

    cs.LG 2026-05 unverdicted novelty 5.0 of 10

    Controlled noise injection into GSM8K rewards for Qwen2.5 models shows persistent validation gaps under compute scaling and asymmetric degradation from false negatives versus false positives.

  13. Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges

    cs.LG 2026-04 unverdicted novelty 5.0 of 10

    The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under op...

  14. Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization

    cs.AI 2026-06 unverdicted novelty 4.0 of 10

    Proxy RL produces a staged proxy-internalization capability that emerges before and predicts reward hacking in coding environments.

Pith tools