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AI Research
arXiv CS.AI·Jun 9·5 min read
Trajectory-Refined Distillation
TL;DR
On-policy distillation (OPD) suffers from a problem called prefix failure, where dense supervision leads to ineffective learning. This work introduces a refined approach to OPD that addresses the issues of bimodal teacher mixtures and fragmented gradients.
✦ Why It Matters
Engineers can apply the refined OPD method to improve training outcomes for large language models.
Key Takeaways
- Adopt TRD to address prefix failure in LLMs.
- Utilize trajectory-level corrections for better learning outcomes.
- Explore alternative outputs to enhance model training.
- Access the TRD code for practical implementation.
How It Works
TRD revises the student's output by correcting problematic prefixes during training, which helps to prevent the issues caused by prefix failure. This approach allows the model to learn from a broader range of valid outputs, improving its overall reasoning and accuracy.