HiRE: Hindsight Reward Editing for Policy Finetuning

2026-09-22

AI Summary

本文提出HiRE方法，通过对比成功与失败轨迹来调整基础表示模型的奖励预测，解决预训练机器人策略在特定环境中微调时面临的奖励质量问题。

Abstract

Pre-trained robot policies always require finetuning to adapt to specific environments. Reinforcement Learning (RL) offers high performance potential because it improves action optimality rather than simply mimicking data. However, such potential depends heavily on reward quality. Sparse rewards lack process feedback, human-designed rewards are costly and biased, and semantic rewards from foundation representations are often not control-centric. We propose Hindsight Reward Editing (HiRE), a training-free framework to break this reward bottleneck. HiRE bridges the broad knowledge of foundation representation models with physical control awareness, by contrasting successful and failed trajectories in hindsight. It calibrates foundation representation models by identifying "trap states" that are predicted as high-rewarding states yet eventually result in failure, and vice versa. HiRE explicitly penalizes these traps while boosting rewards for critical successful states. This approach can be flexibly compatible with any foundation representations and RL algorithms. Experiments show that HiRE consistently outperforms other reward recipes by delivering dense, control-aware feedback that prevents value function collapse and reward hacking, thereby achieving superior sample efficiency, stable policy updates, and higher performance ceilings, e.g., at least 3x performance of the base policies. Qualitative results are at https://hire-project.github.io .

Problem

• Pre-trained Policies
• Reward Quality
• Reinforcement Learning
• Sparse Rewards
• Control-Aware

Innovation

• Hindsight Reward Editing
• Foundation Representation Models
• Reward Calibration
• Control-Aware Feedback
• Policy Finetuning