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New Circuit Reasoning Score improves RL data selection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Circuit Reasoning Score (CRS) to improve data selection for reinforcement learning with verifiable rewards (RLVR). Unlike previous methods that treat data value as intrinsic to problems, CRS assesses data value based on a model's engagement with its reasoning circuits. This approach, tested on Qwen2.5-Math-7B, showed that data with lower reasoning-circuit engagement led to better performance on benchmarks like GSM8K and OlympiadBench. AI

IMPACT This new data selection method could lead to more efficient and effective training of AI models, particularly in complex reasoning tasks.

RANK_REASON Academic paper introducing a new method for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Circuit Reasoning Score improves RL data selection

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Academic paper introducing a new method for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhuofan Chen, Ziqian Jiao, Yikai Cui, Zhixin Cai, Jun Bai, Wenge Rong ·

    CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards

    arXiv:2609.07183v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria--difficulty filtering, hand-curation, reward-trajectory scoring--assess data value as an intri…

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