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Oct 10

Oct 10Sat
  1. meng shaoXAI score79

    Xiaomi's MiMo-V2.6 report explains scaling RL along batch, environments, and grading

    AIXiaomi's MiMo-V2.6 technical report argues that scaling reinforcement learning, not more pretraining data, is the main lever for frontier capability, along batch size, environment diversity, and grader strength.

    Why it matters: The piece walks through the report's three-way RL scaling method, batch size, environments, and grading, with concrete failure modes and stabilization fixes useful to agentic RL practitioners.

  2. ModelScopeOfficialAI score60
    Image from @ModelScope2022's post

    VeriLoop E2, a 27B open-weight reasoning model, is now available

    AIModelScope announces VeriLoop E2, a 27B open-weight model built on Qwen3.8-27B with a 262K native context window.

    Why it matters: The post names the open-weight base model, the verifier-governed training loop, and the benchmark results, so readers can check the claimed gains against comparison models.

Oct 9

Oct 9Fri
  1. InferactOfficialAI score52
    Image from @inferact's post

    vLLM on NVIDIA Vera Rubin NVL72 reaches 7.8x GB200 throughput on MiniMax M3

    AIInferact says vLLM now supports NVIDIA Vera Rubin, with early results showing more than 7.8x the throughput of GB200 on MiniMax M3 at matched interactivity on AgentX. The team says it integrated a Rubin-optimized MSA prefill kernel and locality-aware optimizations, and describes the results as early.