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\n","updatedAt":"2025-04-01T23:24:52.147Z","author":{"_id":"65642d7401de72cb63165d22","avatarUrl":"/avatars/1f4417c4ac5e781ce73eae1060e3f7f2.svg","fullname":"ytaewon","name":"hamzzi","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.5393093228340149},"editors":["hamzzi"],"editorAvatarUrls":["/avatars/1f4417c4ac5e781ce73eae1060e3f7f2.svg"],"reactions":[{"reaction":"❤️","users":["reign12","Kelexine"],"count":2}],"isReport":false}},{"id":"67ede6506047e7ceb40096b1","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2025-04-03T01:37:20.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning](https://huggingface.co/papers/2502.14768) (2025)\n* [R1-Zero's\"Aha Moment\"in Visual Reasoning on a 2B Non-SFT Model](https://huggingface.co/papers/2503.05132) (2025)\n* [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://huggingface.co/papers/2503.14476) (2025)\n* [Understanding R1-Zero-Like Training: A Critical Perspective](https://huggingface.co/papers/2503.20783) (2025)\n* [Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't](https://huggingface.co/papers/2503.16219) (2025)\n* [Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond](https://huggingface.co/papers/2503.10460) (2025)\n* [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://huggingface.co/papers/2503.06749) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
\nThe following papers were recommended by the Semantic Scholar API
\n- \n
- Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning (2025) \n
- R1-Zero's\"Aha Moment\"in Visual Reasoning on a 2B Non-SFT Model (2025) \n
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2025) \n
- Understanding R1-Zero-Like Training: A Critical Perspective (2025) \n
- Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't (2025) \n
- Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond (2025) \n
- Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models (2025) \n
Please give a thumbs up to this comment if you found it helpful!
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Papers
arxiv:2503.24290
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Abstract
Open-Reasoner-Zero achieves superior performance on reasoning benchmarks using a minimalist PPO approach, requiring fewer training steps than DeepSeek-R1-Zero.
AI-generated summary
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 (lambda=1, gamma=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.
Community
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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning (2025)
- R1-Zero's"Aha Moment"in Visual Reasoning on a 2B Non-SFT Model (2025)
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2025)
- Understanding R1-Zero-Like Training: A Critical Perspective (2025)
- Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't (2025)
- Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond (2025)
- Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models (2025)
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