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Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

31 Mar 2025arXiv:2503.24290archive 2025-07-28

Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, Heung-Yeung Shum

We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training focusing on scalability, simplicity and accessibility. Through extensive experiments, we demonstrate that a minimalist approach, vanilla PPO with GAE (λ=1, γ=1) and straightforward rule-based rewards, without any KL regularization, is sufficient to scale up both response length and benchmark performance, similar to the phenomenon observed in DeepSeek-R1-Zero. Using the same base model as DeepSeek-R1-Zero-Qwen-32B, our implementation achieves superior performance on AIME2024, MATH500, and the GPQA Diamond benchmark while demonstrating remarkable efficiency -- requiring only a tenth of the training steps, compared to DeepSeek-R1-Zero pipeline. In the spirit of open source, we release our source code, parameter settings, training data, and model weights across various sizes.

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log_probs_from_logits open-reasoner-zero/open-reasoner-zero/orz/ppo/models.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 959df7824bf09cc1 · report
convert_ring_attn_params open-reasoner-zero/open-reasoner-zero/orz/ppo/models.py community (archive-listed) unverified MIT (permissive) · 71e6ee83ee6c4339 · report
repeatness open-reasoner-zero/open-reasoner-zero/playground/orz_14m_ppo_mini.py community (archive-listed) unverified MIT (permissive) · c3b40f0f038248ce · report
reset_ring_attn_position_ids open-reasoner-zero/open-reasoner-zero/orz/ppo/models.py community (archive-listed) unverified MIT (permissive) · e93117d99aa6b210 · report

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BASEEntropy RegularizationPPO

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