RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
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.
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Kaiqu Liang
admin_assigned
2025-01-17T10:39:14.928Z
650237b5d1b0b0db4f29ae8a
/avatars/8f92cf8f3f1ddb45c2c58c4a59ce4633.svg
KAIQU LIANG
user
kaiquliang
6789d842f16a4dec461a2041
Haimin Hu
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Ryan Liu
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Thomas L. Griffiths
6789d842f16a4dec461a2044
Jaime Fernández Fisac
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
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.
Reinforcement Learning from Hindsight Simulation (RLHS) reduces misalignment and improves user satisfaction by simulating downstream consequences for feedback in generative AI systems.
Reinforcement Learning from Human Feedback (RLHF) Goodhart's Law dynamics sycophancy deception decoupling evaluation hindsight feedback theoretical analysis Reinforcement Learning from Hindsight Simulation (RLHS) Proximal Policy Optimization (PPO) Direct Preference Optimization (DPO) online preference optimization offline preference optimization user study