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BoundaryEvo framework boosts small LLM reasoning with adaptive data selection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BoundaryEvo, a novel framework for improving the long-chain reasoning capabilities of smaller language models through data-model co-evolution. This two-stage approach first primes the student model with basic reasoning skills and then dynamically adapts the training data to the model's evolving proficiency. By focusing on questions the student struggles with but an expert can solve, BoundaryEvo significantly boosts accuracy on reasoning benchmarks, raising Qwen3.5-4B's performance by over 12 percentage points on IMO-AnswerBench using only 1,000 training examples. AI

IMPACT This framework could enable more efficient training of smaller, specialized models for complex reasoning tasks.

RANK_REASON The item describes a new research framework and its performance on benchmarks, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

BoundaryEvo framework boosts small LLM reasoning with adaptive data selection

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The item describes a new research framework and its performance on benchmarks, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiaming Su, Suorong Yang, Xudong Li, Tengchao Yang, Peng Ye ·

    BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation

    arXiv:2610.13285v1 Announce Type: new Abstract: Distilling long-chain reasoning trajectories from large teacher models provides an effective way to improve the reasoning ability of small language models. However, existing methods typically select a fixed training set before disti…

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