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arXiv CS.AI·1d ago·5 min read
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning
TL;DR
TQG-Exp is a new framework for experience-based learning in large language models that incorporates trajectory quality feedback, significantly enhancing performance. It outperforms existing methods, achieving up to 8.55 percentage points improvement in task completion metrics.
✦ Why It Matters
Implement trajectory-quality feedback in your LLM projects to enhance learning outcomes today.
Key Takeaways
- Explore TQG-Exp for improved LLM performance.
- Incorporate trajectory quality feedback in your models.
- Utilize paired rollouts for experience optimization.
- Test on diverse environments like ALFWorld and WebShop.
Full Summary
Experience-based learning allows large language model (LLM) agents to improve by reusing knowledge from past tasks. Traditional methods focus solely on final outcomes, neglecting the quality of the execution process.
TQG-Exp addresses this by introducing Trajectory Quality Loss (TQL), which evaluates both task completion and the quality of the execution trajectory. This framework uses paired rollouts to optimize experience based on trajectory quality, selecting representative trajectories for learning.
Experiments conducted on environments like ALFWorld and WebShop demonstrated that TQG-Exp outperformed existing methods, achieving improvements of 4.50 and 8.55 percentage points in average task success rates for different LLMs. These results suggest that incorporating process feedback can lead to more effective learning in AI systems.