Qwen3.6 27B

Headlines · 2

1. 1d
   Google Researchers Open-Source EnvHarness for Adaptive Agent Training

   Google researchers have open-sourced EnvHarness, a framework that adapts training environments to an agent’s weaknesses without modifying the agent, underlying environment, or evaluation system. It wraps existing environments through standard reset() and step() interfaces and includes EnvRigger to identify weaknesses and generate targeted changes. The researchers reported gains of 2.9–3.7 percentage points across WebArena, SWE-bench Verified, and ALFWorld for several tested models.

   InfoQ 中文 AI&大模型 · 🔥 4
2. Oct 19
   Hands-on: Nemotron 3.5 Bolt suits small multiuser setups with long context

   A Reddit user posting in r/LocalLLaMA as /u/1ncehost says Nemotron 3.5 Bolt was the right size for their 128 GB setup (4x MI100) and works for small multiuser workloads. The user reports that Bolt's decoders are 2-bit quantized with other sensitive parts in NVFP4 in the stock model, and that it pairs one full-attention (GQA) layer with every five linear-attention (Mamba2) layers, giving a narrower attention footprint than Qwen3: about 200 MB at 30k context and 1 GB at 256k, versus roughly 3.4 GB for Flash Next and 8.5 GB for 27B. The poster describes Bolt as essentially a REAP-ed Nemotron 3.5 Super with additional post-training, rates its intelligence as roughly on par with Qwen3.6 27B, says it runs about twice as fast as 27B on their hardware, and singles out its non-hallucination rate and memory-efficient long context and prefix cache.

   r/LocalLLaMA · 🔥 0

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