ReasonFlux vs Distillation

[arunraman]

This is very interesting and I would like to build on it. How is Reasonflux efficient from say a distilled reasoning model for a domain? Can we look at this approach as RAG+ Reasoning? Can we use this approach to enable reasoning for any LLM?

[YangLing0818]

This is very interesting and I would like to build on it. How is Reasonflux efficient from say a distilled reasoning model for a domain? Can we look at this approach as RAG+ Reasoning? Can we use this approach to enable reasoning for any LLM?

Insightful questions! Actually, distilling knowledge from a specific domain like the construction of our template library, also similar to our previous work - Buffer of Thought. Therefore, using such distilled knowledge base can enable your RAG+reasoning more efficient and accurate as demonstrated in our ReasonFlux and Buffer of Thoughts. Also both are general frameworks and enable reasoning for any LLM.

[arunraman]

Thanks @YangLing0818 . When you say we can use a distilled knowledge base, are you referring to replacing your inference LLM with this and still using navigator LLM as well? Also for the navigator LLM, instead of full-blown training, can we just do LoRa?

Btw I have reached out to you via email too for discussion. please let me know if you are open to having a discussion

[YangLing0818]

Thanks @YangLing0818 . When you say we can use a distilled knowledge base, are you referring to replacing your inference LLM with this and still using navigator LLM as well? Also for the navigator LLM, instead of full-blown training, can we just do LoRa?

Btw I have reached out to you via email too for discussion. please let me know if you are open to having a discussion

Using a distilled knowledge base means extracting a template library, and using LoRA also works well.