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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...
This work proposes BiVCoder, a diagnosis-driven multi-agent framework featuring a novel bidirectional code-test diagnosis mechanism, and introduces BiVCoder-SFT, a role-specific instruction fine-tuning scheme.
Large Language Models (LLMs) have demonstrated remarkable potential in automated code generation. However, existing test-driven code generation and refinement frameworks are often hindered by the tests' quality: they typically treat self-generated tests as ground truth, leading to ineffective debugging loops where code...
DBcover is proposed, an LLM-driven database test generation framework that performs white-box, code-aware SQL test generation through contextual reasoning, and substantially outperforms existing fuzzers.
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