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AI Research
arXiv CS.AI·Jun 12·5 min read
Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents
TL;DR
Long-horizon tool-use reinforcement learning struggles with effective credit assignment due to the blending of useful and harmful actions. Sibling-Guided Credit Distillation (SGCD) was developed to improve this by using dynamic sampling of successful and failed rollouts to create a clearer credit reference.
✦ Why It Matters
Engineers can leverage SGCD to improve reinforcement learning models for complex tasks requiring long-term planning.
Key Takeaways
- Implement SGCD for better credit assignment in reinforcement learning.
- Use dynamic sampling to enhance training data diversity.
- Consider external LLMs for summarizing contrasting rollouts.
- Monitor performance metrics to evaluate training effectiveness.
Full Summary
Long-horizon tool-use reinforcement learning involves training agents to use tools effectively over extended sequences of actions. However, traditional methods often lead to poor credit assignment, where both beneficial and detrimental actions are reinforced equally, hindering learning.
To address this, Sibling-Guided Credit Distillation (SGCD) was introduced, which focuses on credit assignment rather than competing actor losses. SGCD employs dynamic sampling to generate a mix of successful and failed rollouts, allowing an external large language model (LLM) to summarize these contrasts into a stepwise credit reference for training.
This method reshapes the advantages of actions taken by the agent, leading to more effective learning. In experiments, SGCD improved performance metrics significantly, such as increasing AppWorld test_normal scores from 42.9 to 45.6 and τ3-airline pass@1 from 0.583 to 0.602.
These findings suggest that SGCD can enhance the training of agents in complex environments.