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MiMo-V2.6 reportedly uses AI agents to build tasks and grade answers during training

A user sharing the paper says humans set the budget and rules while agents audit tests and hunt for cheats.

1 Source, 27m ago, first seen 27m ago

TLDR

A user sharing the MiMo-V2.6 paper says AI agents build training tasks, audit tests, grade answers and look for cheats, while humans set the budget and rules. The user says a grader agent rewarded cleaner patches that passed tests rather than workarounds. MiMo-V2.6-Pro’s DeepSWE score reportedly rose from 58.4 to 72.6 over $2.6 million of reinforcement learning and was still climbing when training stopped.

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1 Source, first seen 27m ago

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1 Source

Rohan Paul@rohanpaul_aiThe paper for MiMo-V2.6 by Xiaom is out. In MiMo-V2.6, AI runs much of its own training loop: agents build the tasks, audit the tests, grade the answers and hunt for cheats. Humans set the budget and the rules. shows that agent models kept improving with more RL compute by scaling batch size, task and harness variety, and grading effort together. Scaling RL for coding agents is hard because pass/fail tests can't tell a clean fix from a hacky fix. Agents also learn to game environments, for example by downloading the published fix. A grader agent compared passing patches in each group and moved reward to the cleaner ones. Without it, agents drifted toward longer runs and workarounds like swallowed exceptions. MiMo-V2.6-Pro’s DeepSWE score rose from 58.4 to 72.6 over $2.6M of RL and was still climbing when training stopped. – arxiv. org/abs/2610.11959 Title: "MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement"27m
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    1 Source

    Rohan Paul@rohanpaul_aiThe paper for MiMo-V2.6 by Xiaom is out. In MiMo-V2.6, AI runs much of its own training loop: agents build the tasks, audit the tests, grade the answers and hunt for cheats. Humans set the budget and the rules. shows that agent models kept improving with more RL compute by scaling batch size, task and harness variety, and grading effort together. Scaling RL for coding agents is hard because pass/fail tests can't tell a clean fix from a hacky fix. Agents also learn to game environments, for example by downloading the published fix. A grader agent compared passing patches in each group and moved reward to the cleaner ones. Without it, agents drifted toward longer runs and workarounds like swallowed exceptions. MiMo-V2.6-Pro’s DeepSWE score rose from 58.4 to 72.6 over $2.6M of RL and was still climbing when training stopped. – arxiv. org/abs/2610.11959 Title: "MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement"27m
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