aiqbench analysis
HindSearch addresses the inefficiency of binary rewards in search-augmented reinforcement learning by introducing a hindsight self-distillation mechanism using frozen judges to critique failed trajectories. This approach converts discarded failure data into an auxiliary distillation signal, significantly improving exact-match accuracy on standard benchmarks without requiring access to gold answers during inference. The method demonstrates that leveraging detailed post-hoc critiques is more effective than simple success/failure signals for training search-based agents.
HindSearch: Trajectory-Level Hindsight Critique for Search-Augmented Reinforcement Learning
arXiv:2608.01597v1 Announce Type: cross Abstract: Search-augmented LM agents are typically trained with a binary exact-match reward, which throws away most of what a failed trajectory tells us about why it failed. We introduce HindSearch, a hindsight self-distillation procedure for GRPO: after each rollout, a frozen judge writes a short critique of every failed trajectory using the gold answer, and the critique supplies an auxiliary on-policy distillation signal on the student's search actions. On the standard seven-benchmark suite with Qwen2.5-3B-Instruct, HindSearch reaches 39.4% average EM, outperforming prior search-RL baselines. Removing the judge's access to the gold answer erases most of the gain, isolating hindsight as the source of the improvement.
Abstract:Search-augmented LM agents are typically trained with a binary exact-match reward, which throws away most of what a failed trajectory tells us about why it failed. We introduce HindSearch, a hindsight self-distillation procedure for GRPO: after each rollout, a frozen judge writes a short critique of every failed trajectory using the gold answer, and the critique supplies an auxiliary on-policy distillation signal on the student's search actions. On the standard seven-benchmark suite with Qwen2.5-3B-Instruct, HindSearch reaches 39.4% average EM, outperforming prior search-RL baselines. Removing the judge's access to the gold answer erases most of the gain, isolating hindsight as the source of the improvement.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
arXiv:2608.01597 [cs.LG]
(or arXiv:2608.01597v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.01597 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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