Summary of Openr: An Open Source Framework For Advanced Reasoning with Large Language Models, by Jun Wang et al.
OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models
by Jun Wang, Meng Fang, Ziyu Wan, Muning Wen, Jiachen Zhu, Anjie Liu, Ziqin Gong, Yan Song, Lei Chen, Lionel M. Ni, Linyi Yang, Ying Wen, Weinan Zhang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This technical report introduces OpenR, an open-source framework for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training, and non-autoregressive decoding into a cohesive software platform. Building on the success of OpenAI’s o1 model, which demonstrated improved reasoning through step-by-step reasoning and reinforcement learning, OpenR integrates test-time compute, reinforcement learning, and process supervision to improve LLM reasoning beyond traditional autoregressive methods. The framework is evaluated on the MATH dataset using publicly available data and search methods, achieving substantial gains in reasoning and performance driven by test-time computation and reinforcement learning through process reward models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OpenR is a new way to make big language models better at thinking and problem-solving. It’s an open-source tool that combines several important ideas together to help large language models learn from experience and get smarter over time. This is similar to how humans learn and improve as they practice and receive feedback. OpenR uses this approach to help language models be more logical and make better decisions. The developers of OpenR tested it on a math problem dataset and found that it worked really well, improving the model’s ability to reason and solve problems. |
Keywords
» Artificial intelligence » Autoregressive » Reinforcement learning
