OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

• URL: http://arxiv.org/abs/2410.09671v1
• Date: Sat, 12 Oct 2024 23:42:16 GMT
• Title: OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models
• Authors: 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,
• Abstract summary: We introduce 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. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI's o1 model with reinforcement learning.
• Score: 61.14336781917986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (both online and offline), and non-autoregressive decoding into a cohesive software platform. Our goal is to establish an open-source platform and community to accelerate the development of LLM reasoning. Inspired by the success of OpenAI's o1 model, which demonstrated improved reasoning abilities through step-by-step reasoning and reinforcement learning, OpenR integrates test-time compute, reinforcement learning, and process supervision to improve reasoning in LLMs. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI's o1 model with reinforcement learning, achieving advanced reasoning capabilities beyond traditional autoregressive methods. We demonstrate the efficacy of OpenR by evaluating it on the MATH dataset, utilising publicly available data and search methods. Our initial experiments confirm substantial gains, with relative improvements in reasoning and performance driven by test-time computation and reinforcement learning through process reward models. The OpenR framework, including code, models, and datasets, is accessible at https://openreasoner.github.io.

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