Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
When Privileged Guidance Misaligns: State-Matched Routing and Contextualized Self-Distillation for Multi-Turn Agents
TL;DR
State-Matched Routing and Contextualized Self-Distillation (SMRC-SD) enhances multi-turn agents by ensuring guidance aligns with the agent's current state, improving task success rates significantly.
✦ Why It Matters
Implement SMRC-SD in your multi-turn agent systems to improve task success rates immediately.
Key Takeaways
- Use SMRC-SD to ensure guidance aligns with the agent's current state.
- Implement state-conditioned teacher context for better supervision.
- Evaluate your multi-turn agents on ALFWorld and WebShop for performance gains.
- Filter out incompatible guidance to avoid state-reference mismatches.
How It Works
SMRC-SD operates by verifying if the student's current execution state matches any state in the reference trajectory. If a match is found, it applies distillation, ensuring that the guidance is relevant and contextually appropriate.
This selective approach prevents the use of outdated or irrelevant references, enhancing the learning process.