General-Reasoner: Advancing LLM Reasoning Across All Domains

★

☆

SHARE

Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

Good to know

Topic Hierarchy

File Information

Paper Summary

Paperzilla title

Chatbots Get Smart Beyond Math (Still in Beta!)

This paper introduces GENERAL-REASONER, a novel training approach that significantly enhances large language models' (LLMs) reasoning capabilities across diverse domains beyond just math and coding. The method leverages a large, verifiable dataset curated from web crawling and a generative model-based verifier to provide robust reward signals for reinforcement learning. The results demonstrate superior generalizable reasoning performance compared to existing open-source baselines, while maintaining effectiveness in mathematical tasks.

Possible Conflicts of Interest

Several authors (Xueguang Ma, Qian Liu, Dongfu Jiang, Ge Zhang, Zejun Ma, Wenhu Chen) are affiliated with TikTok, Singapore. TikTok is a commercial entity, and its involvement in AI research, especially concerning large language models, could present a conflict of interest as the research might directly benefit the company's products or strategic direction.

Identified Weaknesses

Work in Progress / Technical Report Status

The paper is explicitly labeled as a 'Technical Report. Work in progress.', indicating it has not undergone formal peer review, which is a standard for published scientific work. This implies potential for unaddressed issues or unverified claims.

Limited Scope for Specialized Reasoning

The study explicitly states it does not specifically focus on code reasoning or olympiad-level math competitions, limiting the generalizability of its 'all domains' claim to these specific advanced reasoning types.

Performance Gap with Closed-Source Models

While outperforming open-source baselines, the authors note that 'a performance gap remains on some benchmarks compared to closed-source or closed-data models,' indicating that the proposed method is not yet state-of-the-art across all measures when compared to top commercial models.

Computational Cost of Verifier

Although the generative verifier is described as 'compact' (1.5B parameters), it still requires dedicated GPU resources during training (e.g., 2 GPUs per node in earlier vLLM versions), adding to the computational overhead for large-scale RL training.

Rating Explanation

The paper presents a novel and effective approach to expand LLM reasoning to diverse domains with strong empirical results against open-source baselines. However, it is explicitly a 'Technical Report. Work in progress,' which implies it has not undergone formal peer review. Additionally, the affiliation of several authors with TikTok, a commercial entity, introduces a potential conflict of interest, preventing a higher rating.

Good to know

This is our free standard analysis. Paperzilla Pro fact-checks every citation, researches author backgrounds and funding sources, and uses advanced AI reasoning for more thorough insights.

Explore Pro →