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 b...
| Authors | Hui-Ze Tan, Xiao-Wen Yang, Hao Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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