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Qwen3.6 CoT tuning seeks helpers
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REDDIT · REDDIT// 5h agoNEWS

Qwen3.6 CoT tuning seeks helpers

A LocalLLaMA user is looking for collaborators or advice on using chain-of-thought data to improve Qwen3.6-35B-A3B, Alibaba’s sparse 35B MoE model with roughly 3B active parameters. The post is thin on implementation details, but it lands amid growing community interest in local reasoning and agentic-coding fine-tunes around Qwen 3.6.

// ANALYSIS

This is more signal than announcement: the real story is that Qwen3.6-35B-A3B is already becoming a community fine-tuning target, not just a model people benchmark once and forget.

  • CoT tuning could improve reasoning behavior, but data provenance and contamination risk matter more than raw trace volume
  • MoE routing makes fine-tuning trickier than dense-model tinkering, especially if the goal is stable long-form reasoning instead of benchmark overfitting
  • The model’s 35B total / 3B active profile is attractive because serious local experiments can happen without frontier-scale compute
  • With no repo, dataset, eval plan, or baseline results shared, developers should treat this as an early collaboration call rather than a usable release
// TAGS
qwen3-6-35b-a3bllmreasoningfine-tuningopen-weightslocal-llm

DISCOVERED

5h ago

2026-04-22

PUBLISHED

5h ago

2026-04-21

RELEVANCE

5/ 10

AUTHOR

Purpose-Effective