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Self-Evolving Coding Agents via Harness-Policy Optimization research paper by Microsoft, 2026

Microsoft · Training and scaling · Oct 16, 2026 · 48 upvotes · unverified

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What it shows

This framework improves coding agents by recursively alternating between optimizing the agent's policy and the environment harness it uses to interact with code.

By Zhepei Wei, Wenlin Yao, Baolin Peng and 5 more · arXiv 2610.22791 · PDF · Code

UnverifiedSummarised by a model from the abstract; not yet checked by hand.

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About this paper
Citations
Not counted yet · Semantic Scholar
Upvotes
48 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

UpvotesNov 9, 2026Hugging Face upvotes fell from 53 to 48.
UpvotesNov 2, 2026Hugging Face upvotes fell from 55 to 53.
New summaryOct 26, 2026This framework improves coding agents by recursively alternating between optimizing the agent's policy and the environment harness it uses to interact with code.
TopicOct 26, 2026Moved from Agents and evaluation to Training and scaling.
New paperOct 26, 2026Found by the weekly Hugging Face scan; not yet checked by hand.