AINewsnow

Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune!

This story is from 2026-09-05. It is preserved in the archive; the latest stories are on the live feed.

Coverage of "Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune!" from 2 sources, with a live timeline of who reported what and when.

Read the full story at r/learnmachinelearning ↗

Timeline · 2 reports

  1. 2026-09-06 18:41 · r/MachineLearning
    Proposed architecture for inferencing sparse MOE models increasing Active parameters using layered + linear decay. Succinct reasoning without any model training or fine tune. [p]
  2. 2026-09-05 07:16 · r/learnmachinelearning
    Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune!

More stories

  1. M2 Mac ultra128gb Qwen flash next — r/LocalLLM
  2. Multi-hour llama.cpp optimization experiments on Qwen MoE models, patches, benchmarks, and reproduction guides — r/LocalLLM
  3. The bear can dance: Qwen 3.8 27B on one 3090 for 3 weeks — r/LocalLLaMA
  4. CUDA: enable sparse fa for qwen4 by am17an · Pull Request #28770 · ggml-org/llama.cpp — r/LocalLLaMA
  5. M1 Max 32GB, trying to run Qwen 3.8 27B at decent speeds and context — r/LocalLLaMA
  6. [Guide / Weights] Qwen 3.8 27B on Intel Arc: Why IQ quants crawl at 8 tok/s, why Q4_K outpaces sub-4bpw on Battlemage, and clean RCO GGUFs (16GB & 24GB) — r/LocalLLM
  7. My Version of Jev running locally, playing doom. — r/LocalLLM
  8. Two node BC250 cluster comparison of Qwen3.6 vs Qwen 3.8 — r/LocalLLM

Get the daily brief of stories like this at 6:30 every morning →