Hindsight Credit Assignment for Long-Horizon LLM Agents
Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bottlenecks: inaccurate step-level Q-value estimation and misaligned value baselines for intermediate states. To address these limitations, we introduce HCAPO, the first framework to integrate hindsight credit assignment into LLM agents. HCAPO leverages the LLM itself as a post-hoc critic to refine step-level Q-values through hindsight reasoning. Furthermore, HCAPO's multi-scale advantage mechanism effectively supplements the inaccurate value baselines at critical decision states. Evaluations across three challenging benchmarks, including WebShop and ALFWorld, demonstrate that HCAPO consistently outperforms state-of-the-art RL methods. Notably, HCAPO achieves a 7.7% improvement in success rate on WebShop and a 13.8% on ALFWorld over GRPO using the Qwen2.5-7B-Instruct model. These results indicate that HCAPO significantly enhances exploration efficiency, promotes concise decision-making, and ensures scalability in complex, long-horizon tasks.
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Hui-Ze Tan
69b27c59206bad330cd800de
Xiao-Wen Yang
admin_assigned
2026-03-12T10:16:31.777Z
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Xiao-Wen Yang
yangxw
user
yangxw
69b27c59206bad330cd800df
Hao Chen
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Jie-Jing Shao
admin_assigned
2026-03-12T10:16:38.867Z
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Jie-Jing Shao
shjj
user
shjj
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Yi Wen
69b27c59206bad330cd800e2
Yuteng Shen
69b27c59206bad330cd800e3
Weihong Luo
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Xiku Du
69b27c59206bad330cd800e5
Lan-Zhe Guo
69b27c59206bad330cd800e6
Yu-Feng Li
Hindsight Credit Assignment for Long-Horizon LLM Agents
Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bottlenecks: inaccurate step-level Q-value estimation and misaligned value baselines for intermediate states. To address these limitations, we introduce HCAPO, the first framework to integrate hindsight credit assignment into LLM agents. HCAPO leverages the LLM itself as a post-hoc critic to refine step-level Q-values through hindsight reasoning. Furthermore, HCAPO's multi-scale advantage mechanism effectively supplements the inaccurate value baselines at critical decision states. Evaluations across three challenging benchmarks, including WebShop and ALFWorld, demonstrate that HCAPO consistently outperforms state-of-the-art RL methods. Notably, HCAPO achieves a 7.7% improvement in success rate on WebShop and a 13.8% on ALFWorld over GRPO using the Qwen2.5-7B-Instruct model. These results indicate that HCAPO significantly enhances exploration efficiency, promotes concise decision-making, and ensures scalability in complex, long-horizon tasks.
HCAPO addresses credit assignment challenges in LLM agents through hindsight credit assignment and multi-scale advantage mechanisms, improving performance in long-horizon tasks.
large language model agents credit assignment sparse rewards Group Relative Policy Optimization hindsight credit assignment post-hoc critic step-level Q-value estimation value baselines multi-scale advantage mechanism exploration efficiency decision-making scalability