Distill Until Divergence: Error-Truncated Hindsight Distillation for Agentic RL
In brief
- Introduces error-truncated hindsight distillation for agentic reinforcement learning
- Uses positive and negative hindsight skills to correct biases in existing methods
- Enhances performance and fine-grained credit assignment in long trajectories
This study introduces a distillation method for agentic reinforcement learning that corrects biases in existing approaches. It leverages positive and negative hindsight skills to finely guide intermediate decisions. Experimental results demonstrate improved performance and precise credit allocation.
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