ByteDance’s SWE-Pruner Pro cuts coder-agent tokens by 39% without losing quality

ByteDance · hf · 2026-07-21

### What it claims ByteDance and collaborators show that a coder LLM already learns useful internal signals for deciding what tool output to keep or prune. ### Proposed method - SWE-Pruner Pro moves pruning inside the agent instead of attaching a separate code classifier. - A small head reads the agent’s own internal representations and predicts keep/prune labels line by line. - A length-aware embedding accounts for the number of lines in each tool output. ### Results - Across two open-weight backbones and four multi-turn benchmarks, the method saves up to **39%** of prompt and completion tokens while keeping task quality intact. - In addition, on **MiMo-V2-Flash**, it improves SWE-Bench Verified resolve rate by **+3.8%** and Oolong long-context accuracy by **+2.2 points**.

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