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CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR
arXiv – CS AI|Sijia Cui, Pengyu Cheng, Jiajun Song, Yongbo Gai, Guojun Zhang, Zhechao Yu, Jianhe Lin, Xiaoxi Jiang, Guanjun Jiang|
🤖AI Summary
Researchers introduce CLIPO (Contrastive Learning in Policy Optimization), a new method that improves upon Reinforcement Learning with Verifiable Rewards (RLVR) for training Large Language Models. CLIPO addresses hallucination and answer-copying issues by incorporating contrastive learning to better capture correct reasoning patterns across multiple solution paths.
Key Takeaways
- →CLIPO enhances RLVR by adding contrastive learning mechanisms to improve LLM reasoning capabilities.
- →Traditional RLVR only considers final answers, ignoring correctness of intermediate reasoning steps.
- →The new approach reduces hallucination and answer-copying problems in language model training.
- →CLIPO demonstrates consistent improvements across multiple reasoning benchmarks and baselines.
- →The method provides more robust cross-trajectory regularization compared to single-path supervision.
#llm#reinforcement-learning#contrastive-learning#reasoning#policy-optimization#rlvr#clipo#machine-learning#research
Read Original →via arXiv – CS AI
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