KwaiKAT Team Releases KAT-Coder-V2.5: An Agentic Coding Model Trained on 100,000+ Verifiable Repository Environments
100,000+ verifiable environments. That's how KwaiKAT's new coding agent hit 57.2% success — proving infrastructure, not scale, is the real bottleneck.
Why it matters
KAT-Coder-V2.5 challenges the conventional wisdom that model size drives agentic coding capability. By focusing on training infrastructure (AutoBuilder, sandbox audit), Kuaishou achieved dramatic improvements in environment reliability and RL feedback accuracy — signaling a shift in how teams should architect coding AI systems.
The key facts
5 to knowKwaiKAT (Kuaishou) released KAT-Coder-V2.5 technical report
AutoBuilder increased environment construction success from 16.5% to 57.2%
100,000+ verifiable repository environments across 12 languages
Sandbox audit reduced RL feedback errors from ~16% to below 2%
Core thesis: agentic coding bottlenecked by training infrastructure, not model scale
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Publisher excerpt: The KwaiKAT Team at Kuaishou has published the KAT-Coder-V2.5 technical report, arguing that agentic coding capability is bottlenecked by training infrastructure rather than model scale. AutoBuilder raised environment construction success from 16.5% to 57.2%, producing over 100,000 verifiable…