AI RESEARCH ANALYSIS
General-Reasoner: Advancing LLM Reasoning Across All Domains
Authors: Xueguang Ma, Qian Liu, Dongfu Jiang, Ge Zhang, Zejun Ma, Wenhu Chen
This paper introduces GENERAL-REASONER, a novel training paradigm designed to enhance LLM reasoning capabilities across diverse domains. It leverages a large-scale, high-quality dataset of verifiable questions and a generative model-based verifier to achieve robust and generalizable reasoning performance, outperforming existing baselines.
Executive Impact & Key Takeaways
GENERAL-REASONER significantly broadens the application of LLM reasoning beyond traditional math and coding tasks, offering a robust solution for diverse enterprise challenges.
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Reasoning Performance Boost (SuperGPQA)
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Cross-Domain Reasoning Improvement (TheoremQA)
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High-Quality Training Questions
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Model-Based Verifier Parameters
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Zero-RL for LLMs
Diverse Data Curation
Model-Based Verification
Cross-Domain Generalization
Zero Reinforcement Learning for LLMs
The paper builds upon the "Zero" reinforcement learning setting, which allows direct RL training of base LLMs without an intermediate supervised fine-tuning stage. This approach, exemplified by Deepseek-R1-Zero, is efficient as it only requires verifiable question-answer pairs, eliminating the need for complex reasoning chains as training targets. GENERAL-REASONER extends this by applying it to broader, diverse domains, showcasing its adaptability and efficiency for enterprise-level AI systems seeking to rapidly enhance reasoning without extensive data annotation.
Large-Scale Diverse Data Curation
A major contribution is the construction of a large-scale, high-quality dataset of 230,000+ verifiable reasoning questions. This dataset, curated by web crawling and filtering based on WebInstruct, spans disciplines like physics, chemistry, social sciences, and finance—moving beyond the mathematical and coding focus of prior works. For enterprises, this means LLMs can be trained on proprietary data from various departments, enabling multi-faceted problem-solving capabilities rather than siloed expertise.
Generative Model-Based Verification
The paper introduces a compact 1.5B-parameter generative verifier model, "General-Verifier," explicitly trained for chain-of-thought and context-aware answer verification. This replaces traditional rule-based methods, which struggle with diverse answer representations common in real-world scenarios. By leveraging a model-based verifier, businesses can ensure robust and reliable reward signals for RL training, enabling LLMs to learn from complex, varied outputs, and verify solutions in domains where exact matches are rare.
Robust Cross-Domain Generalization
Comprehensive evaluations across 12 benchmarks (including MMLU-Pro, GPQA, SuperGPQA, TheoremQA, and MATH AMC) demonstrate that GENERAL-REASONER consistently outperforms existing baselines. It achieves robust and generalizable reasoning performance across diverse domains while maintaining superior effectiveness in mathematical reasoning. This generalization is crucial for enterprise AI, allowing a single LLM to tackle varied tasks from financial analysis to scientific research, reducing the need for specialized models and streamlining operations.
Enterprise Process Flow: Data Creation Pipeline
WebInstruct w/ Human Answer
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QA Pairs
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Extract (LLM)
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Tag & Solve (LLM)
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8 x CoT Solutions
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Remove (LLM)
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230k diverse, verifiable QA pairs (WebInstruct-Verified)
Key Performance Indicator
66.6% MMLU-Pro Score (Qwen2.5-14B) - General-Reasoner vs. Qwen2.5-14B-Instruct (62.7%)| Verifier Type | Average Agreement Rate |
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| Rule-Based Verifier | 22.2% |
| Model-Based Verifier (General-Verifier) | 78.7% |
Case Study: Impact of Data Abundance and Domain Diversity
Training on a diverse, all-domain dataset significantly enhances general reasoning capabilities while maintaining or improving mathematical reasoning. For the Qwen2.5-14B-Base backbone, using Full diverse data resulted in MMLU-Pro of 66.6%, GPQA of 43.4%, SuperGPQA of 39.5%, and Math-Related of 53.9%. In contrast, training on Math Only data yielded lower scores across general benchmarks: MMLU-Pro 64.8%, GPQA 38.9%, SuperGPQA 35.6%, while Math-Related was 48.6%. This clearly demonstrates the benefit of diverse training data for robust and generalizable reasoning.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with advanced AI reasoning capabilities.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A typical journey to integrate General-Reasoner capabilities into your enterprise systems.
Phase 01: Discovery & Strategy
Initial consultation to understand your specific reasoning challenges, data landscape, and strategic objectives. Define KPIs and success metrics for AI integration.
Phase 02: Data Preparation & Model Customization
Assist in curating and preparing your enterprise-specific data for diverse-domain training. Customize General-Reasoner models to align with your unique operational context.
Phase 03: Deployment & Integration
Seamless integration of the General-Reasoner solution into your existing AI infrastructure and workflows. Ensure compatibility and scalability with your current systems.
Phase 04: Performance Monitoring & Optimization
Continuous monitoring of model performance across diverse reasoning tasks. Iterative optimization based on real-world feedback to maximize efficiency and ROI.
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