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New SWE-Trail method enhances coding agent training data collection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SWE-Trail, a novel method for collecting training data for coding agents. This approach uses historical data from previous attempts to solve issues to guide the collection of new trajectories, without exposing this history directly to the agent. This prevents a train-test mismatch where agents might rely on advice unavailable during actual use. When applied to a 124B student model initialized from Ling-3.0-flash-base, SWE-Trail demonstrated significant improvements in problem resolution rates on the SWE-bench Multilingual and SWE-bench Pro benchmarks. AI

IMPACT Enhances the efficiency and effectiveness of training data collection for coding AI agents, potentially leading to more capable code-generation tools.

RANK_REASON The cluster describes a new research paper detailing a novel method for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

New SWE-Trail method enhances coding agent training data collection

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The cluster describes a new research paper detailing a novel method for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SWE-Trail: History-Guided Trajectory Collection for Coding Agent Training

    SWE-Trail improves trajectory collection for coding agents by using past rollouts to guide exploration without exposing history to the agent, yielding better-trained student models.

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