Nemotron-3.5

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1. 5d
   vLLM open-sources bitwise-consistent RL training kernels and agent skill

   vLLM published a method that makes RL training and inference bitwise consistent: the training forward calls vLLM's inference kernels directly, and those high-performance kernels are modified so rollout and training produce identical logprobs for every sampled token. The team reports that on DeepSeek-V4-Flash its high-performance baseline's train-inference KL rose from 2e-3 to 29.9 while reward fell back, whereas the consistent run kept diff at 0 for 100 steps at 91.3% of the baseline's rollout throughput. On Nemotron-3.5, a Mamba hybrid with NVFP4, the consistent run's SWE-bench Pro score held around 0.6 while the baseline dropped from 0.56 to 0.22; the workflow is released as an agent skill with recipes and code, and DeepSeek-V4.1 has reached 0 diff with performance work still in progress.

   vLLM Blog · 🔥 0

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