How to measure RL gains on software engineering tasks
A post dated September 21, 2026 reports that MiMo Pro's DeepSWE score rose from 58.41 to 72.57 after reinforcement learning runs.
Source: MiMo Pro melhora sua pontuação no DeepSWE após runs de RL (x.com). Text prepared with AI from this source.
What happened and what to do
The post reports that, after reinforcement learning (RL) runs, MiMo Pro's DeepSWE score rose from 58.41 to 72.57. It also compares the result with a score of 74 attributed to Astra, Gemini 3.8 Flash, and Opus 5. The post is dated September 21, 2026.
For a company evaluating models or coding agents, a practical response is to build a reproducible benchmark process: record versions, configurations, runs, and scores, then track changes on a dashboard. This supports comparisons across RL experiments and helps determine whether a system is ready for a real workflow.
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Wendelmaques can diagnose how your company evaluates models and agents, define a reproducible benchmark scope, and implement data collection, monitoring, and a results dashboard on your infrastructure.
Next step
Send a short description of your case to receive a scoped proposal for structuring or improving your evaluation of models and agents.
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