title: RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
summary: Generative AI systems like foundation models (FMs) must align well with human
  values to ensure their behavior is helpful and trustworthy. While Reinforcement
  Learning from Human Feedback (RLHF) has shown promise for optimizing model
  performance using human judgments, existing RLHF pipelines predominantly rely
  on immediate feedback, which can fail to accurately reflect the downstream
  impact of an interaction on users' utility. We demonstrate that feedback based
  on evaluators' foresight estimates of downstream consequences systematically
  induces Goodhart's Law dynamics, incentivizing misaligned behaviors like
  sycophancy and deception and ultimately degrading user outcomes. To alleviate
  this, we propose decoupling evaluation from prediction by refocusing RLHF on
  hindsight feedback. Our theoretical analysis reveals that conditioning
  evaluator feedback on downstream observations mitigates misalignment and
  improves expected human utility, even when these observations are simulated by
  the AI system itself. To leverage this insight in a practical alignment
  algorithm, we introduce Reinforcement Learning from Hindsight Simulation
  (RLHS), which first simulates plausible consequences and then elicits feedback
  to assess what behaviors were genuinely beneficial in hindsight. We apply RLHS
  to two widely-employed online and offline preference optimization methods --
  Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) --
  and show empirically that misalignment is significantly reduced with both
  methods. Through an online human user study, we show that RLHS consistently
  outperforms RLHF in helping users achieve their goals and earns higher
  satisfaction ratings, despite being trained solely with simulated hindsight
  feedback. These results underscore the importance of focusing on long-term
  consequences, even simulated ones, to mitigate misalignment in RLHF.
paper_authors._id: 6789d842f16a4dec461a2040
paper_authors.name: Kaiqu Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:39:14.928Z
paper_authors._id: 650237b5d1b0b0db4f29ae8a
paper_authors.avatarUrl: /avatars/8f92cf8f3f1ddb45c2c58c4a59ce4633.svg
paper_authors.fullname: KAIQU LIANG
paper_authors.type: user
paper_authors.user: kaiquliang
paper_authors._id: 6789d842f16a4dec461a2041
paper_authors.name: Haimin Hu
paper_authors._id: 6789d842f16a4dec461a2042
paper_authors.name: Ryan Liu
paper_authors._id: 6789d842f16a4dec461a2043
paper_authors.name: Thomas L. Griffiths
paper_authors._id: 6789d842f16a4dec461a2044
paper_authors.name: Jaime Fernández Fisac
paper_title: RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
paper_summary: Generative AI systems like foundation models (FMs) must align well with human
  values to ensure their behavior is helpful and trustworthy. While Reinforcement
  Learning from Human Feedback (RLHF) has shown promise for optimizing model
  performance using human judgments, existing RLHF pipelines predominantly rely
  on immediate feedback, which can fail to accurately reflect the downstream
  impact of an interaction on users' utility. We demonstrate that feedback based
  on evaluators' foresight estimates of downstream consequences systematically
  induces Goodhart's Law dynamics, incentivizing misaligned behaviors like
  sycophancy and deception and ultimately degrading user outcomes. To alleviate
  this, we propose decoupling evaluation from prediction by refocusing RLHF on
  hindsight feedback. Our theoretical analysis reveals that conditioning
  evaluator feedback on downstream observations mitigates misalignment and
  improves expected human utility, even when these observations are simulated by
  the AI system itself. To leverage this insight in a practical alignment
  algorithm, we introduce Reinforcement Learning from Hindsight Simulation
  (RLHS), which first simulates plausible consequences and then elicits feedback
  to assess what behaviors were genuinely beneficial in hindsight. We apply RLHS
  to two widely-employed online and offline preference optimization methods --
  Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) --
  and show empirically that misalignment is significantly reduced with both
  methods. Through an online human user study, we show that RLHS consistently
  outperforms RLHF in helping users achieve their goals and earns higher
  satisfaction ratings, despite being trained solely with simulated hindsight
  feedback. These results underscore the importance of focusing on long-term
  consequences, even simulated ones, to mitigate misalignment in RLHF.
paper_ai_keywords: Reinforcement Learning from Human Feedback (RLHF) Goodhart's Law sycophancy deception hindsight feedback Reinforcement Learning from Hindsight Simulation (RLHS) Proximal Policy Optimization (PPO) Direct Preference Optimization (DPO)