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RLP: Reinforcement as a Pretraining Objective
arXiv – CS AI|Ali Hatamizadeh, Syeda Nahida Akter, Shrimai Prabhumoye, Jan Kautz, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi||3 views
🤖AI Summary
Researchers introduce RLP (Reinforcement Learning Pretraining), a new training method that incorporates reinforcement learning exploration into the pretraining phase rather than only post-training. The approach treats chain-of-thought reasoning as exploratory actions and achieved 19% performance improvements on math and science benchmarks across different model architectures.
Key Takeaways
- →RLP integrates reinforcement learning into the pretraining phase, encouraging models to develop independent thinking behavior earlier in training.
- →The method treats chain-of-thought reasoning as exploratory actions with rewards based on information gain for predicting future tokens.
- →Testing on Qwen3-1.7B-Base showed 19% improvement across eight math and science benchmarks.
- →The approach demonstrated scalability across different architectures, with Nemotron-Nano-12B-v2 improving from 42.81% to 61.32% average performance.
- →RLP provides a verifier-free dense reward signal that allows efficient training on full document streams during pretraining.
#reinforcement-learning#pretraining#chain-of-thought#reasoning#llm#research#performance#qwen#nemotron#arxiv
Read Original →via arXiv – CS AI
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