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Evidence record 7360 · automatically gathered

Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such as Group Relative Policy Optimization (GRPO) face a critical dilemma in sparse-reward settings: pure Reinforcement Learning (RL) suffers from advantage collapse and high-variance gradient estimation, while mixed-policy optimization introduces persistent distributional bias. To resolve this dilemma, we introduce Hindsight-Anchored Policy O

Record details

Published: 11 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (11 March 2026), “Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings,” evidence record 7360, https://ethics.ai/record/7360 (originally published by arXiv).

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