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The MiMo-V2.6 series is introduced, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute by building infrastructure for mixed-task agentic RL, including a unified trajectory representation, high-concurrency multi-framework rollout, decoupled control and data planes, and training-inf...
Reinforcement learning (RL) for code agents often uses executable tests to provide binary rewards. With these rewards, Group Relative Policy Optimization (GRPO) assigns identical advantages to test-passing trajectories within each rollout group, overlooking differences in implementation quality and adherence to task re...
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better...
This work proposes SafeEvolve, which checks each skill when it is written and when a later task retrieves it, and introduces SkillMisevo-Gym, which clears every task session but keeps the skill library, so each of these steps can be checked separately.
The Personal Agent Sycophancy Benchmark (PASB) is introduced, a 1,600-task benchmark that traces whether a conversational claim is accepted, written into durable agent state, and reused in a later neutral query, and shows that agent sycophancy is fundamentally a state-writing governance problem.
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