General-Reasoner: Advancing LLM Reasoning Across All Domains
Xueguang Ma, Qian Liu, Dongfu Jiang, Ge Zhang, Zejun MA, Wenhu Chen
Reinforcement learning (RL) has recently demonstrated strong potential in enhancing the reasoning capabilities of large language models (LLMs).Particularly, the ``Zero'' reinforcement learning introduced by Deepseek-R1-Zero, enables direct RL training of base LLMs without relying on an intermediate supervised fine-tuning stage.Despite these advancements, current works for LLM reasoning mainly focus on mathematical and coding domains, largely due to data abundance and the ease of answer verification.This limits the applicability and generalization of such models to broader domains, where questions often have diverse answer representations, and data is more scarce.In this paper, we propose \model, a novel training paradigm designed to enhance LLM reasoning capabilities across diverse domains. Our key contributions include: (1) constructing a large-scale, high-quality dataset of questions with verifiable answers curated by web crawling, covering a wide range of disciplines; and (2) developing a generative model-based answer verifier, which replaces traditional rule-based verification with the capability of chain-of-thought and context-awareness. Our comprehensive evaluation across benchmarks such as MMLU-Pro, GPQA, SuperGPQA, BBEH and MATH, AMC, etc demonstrates that \model outperforms existing baseline methods, achieving robust and generalizable reasoning performance while maintaining superior effectiveness in mathematical reasoning tasks. Code, data, and model checkpoints in this work will be released.