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arXiv · 2607.16257

From Outcomes to Actions: Leveraging Hindsight for Long-Horizon Language Agent Training

Zishang Jiang·Tingyun Li·Jinyi Han·Xinyi Wang·Sihang Jiang·Yizhou Ying·Xiaojun Meng·Jiansheng Wei·Jiaqing Liang·Yanghua Xiao
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Abstract

Reinforcement learning (RL) has become a widely adopted technique for improving large language models (LLMs) on complex tasks. Despite this progress, existing RL methods still face challenges in training agents with longer-horizon interactions. One major bottleneck is distinguishing the contribution of different actions in long-horizon interaction, leading to high optimization variance. To address this, we introduce a novel policy gradient method, Hindsight Policy Optimization (HPO), that projects both the current policy distribution and the hindsight distribution into an intent space and extracts low-variance learning signals from the Wasserstein distance between them. We theoretically and empirically show that aggregating semantically similar states and actions in the intent space yields a bounded-variance estimator and improves policy performance stably. Our code is available online.

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Zishang Jiang, Tingyun Li, Jinyi Han, Xinyi Wang, Sihang Jiang, Yizhou Ying, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao. 2026-06-28. From Outcomes to Actions: Leveraging Hindsight for Long-Horizon Language Agent Training. https://arxiv.org/abs/2607.16257

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