Proposal for Fine-Tuning Fast Open Models via Large Reasoning Model Distillation

Discussion
1

Hi Community, good day to everyone!
I recently watched a thought‑provoking video by Theo (https://www.youtube.com/watch?v=jCv0KSxMqlo) on model this topic. Inspired by his approach, I’d like to open a discussion about using Qwen as a base model for a new, fine‑tunable system—one that “learns” from larger reasoning models [LRMs], e.g. O3, O3-mini, O4 and O4‑Mini, and inherits their problem‑solving strengths while maintaining Qwen’s speed and cost advantages.

Why Qwen as a foundation?

  • As Theo explained in his video, it’s the best fit for open models as its Apache 2.0 Licensed. Meaning, unlike Llama, it won’t impose weird License requirements on organizations if their apps become unicorns.

How distillation from LRMs could help

  • Enhanced reasoning and coherence. By using LRMs as “teachers,” we can generate high‑quality question‐answer and chain‑of‑thought examples, then train Qwen to mimic those patterns.
  • Better tool usage. We fine‑tune Qwen on transcripts where the teacher models successfully invoke and parse tools (code execution, SQL, math solvers), giving Qwen a more robust tool‑calling policy.
  • Reduced hallucinations. Training Qwen on expert outputs from LRMs may shrink its tendency to fabricate facts, boosting reliability (not sure about this tho).

Other potential advantages

  • Rapid iteration. Smaller models train faster so we can run more experiments in less time, testing different distillation recipes and tool‑integration strategies.
  • Lower cloud bill. Every microsecond of inference saved is money in production; Qwen’s efficiency pays dividends at scale. (I believe Groq is a pretty good place to host it)
  • Community extensibility. A fine‑tuned Qwen fork could become incredibly powerfull, specially if other companies enter the game and contribute their training data to the open model. A good example of this is Zed’s Zeta

Discussion points & questions

  1. Is it viable for cursor to implement something like this? Would the community use it? (I know I would)
  2. Has anyone already tried a similar distillation pipeline (O4→Qwen or similar LLMs)? What were the pitfalls? Does it make economic sense?

Best Regards

FYI: Not an AI Engineer, Information in this post comes from Research I made myself; therefore, It might come with some errors.