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arXiv CS.AI·Jun 9·5 min read
CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning
TL;DR
Reinforcement learning with verifiable rewards (RLVR) struggles with optimizing reasoning tasks over static problem sets, leading to inefficient use of resources. CLPO (Curriculum Learning meets Policy Optimization) is introduced as a self-evolving framework that categorizes problems by difficulty using on-policy rollout accuracy.
✦ Why It Matters
Engineers can implement CLPO to improve the efficiency of training large language models by focusing on appropriately challenging tasks.
Key Takeaways
- Utilize CLPO to enhance LLM reasoning through dynamic task adjustment.
- Categorize training tasks into solved, medium, and hard for effective learning.
- Simplify hard problems and diversify medium ones to boost model performance.
- Achieve significant performance gains without additional human annotations.
How It Works
CLPO employs a self-evolving curriculum that adjusts training tasks based on the model's performance. It categorizes problems into three difficulty levels: solved, medium, and hard.
By simplifying hard problems and diversifying medium ones, CLPO ensures that the model is always challenged appropriately, optimizing learning efficiency. The framework also assigns credit for task restructuring based on improvements in downstream accuracy, allowing for continuous adaptation without requiring new human annotations.