HCAPO: Improving the Efficiency of LLM Agents

Credit assignment management presents a significant challenge for Large Language Model (LLM) based agents when operating in multi-step tasks with extended time horizons and sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), often encounter difficulties in obtaining accurate estimates of step-level Q-values and aligning value baselines for intermediate states.

To overcome these limitations, HCAPO, a framework that integrates hindsight credit assignment into LLM agents, has been introduced. HCAPO uses the LLM itself as a post-hoc critic to refine step-level Q-values through reasoning based on the analysis of the results obtained. Furthermore, HCAPO's multi-scale advantage mechanism supports value baselines, which are often inaccurate, in critical decision states.

Evaluations on complex benchmarks such as WebShop and ALFWorld demonstrate that HCAPO consistently outperforms state-of-the-art reinforcement learning (RL) methods. In particular, HCAPO achieved a 7.7% improvement in success rate on WebShop and a 13.8% on ALFWorld compared to GRPO, using the Qwen2.5-7B-Instruct model. These results suggest that HCAPO significantly improves exploration efficiency, promotes more concise decision-making, and ensures scalability in complex, long-horizon tasks.

For those evaluating on-premise deployments, there are trade-offs to consider. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.