Edge 377: LLM Reasoning with Reinforced Fine-Tuning
A very recent LLM reasoning technique created by ByteDance research.
In this Issue:
An overview of reinforced fine-tuning(ReFT) as a method for LLM reasoning.
A review of ReFT’s original paper published by ByteDance.
An introduction to Guardrails AI as one of the most complete frameworks to guide the behavior of LLM applications.
💡 ML Concept of the Day: Reinforced Fine-Tuning and LLM Reasoning
In the last installment of our series about LLM reasoning, we are going to discuss a new technique recently introduced by ByteDance. Reinforced Fine-Tuning(ReFT) looks to address some of the limitation of supervised fine tuning(SFT) approaches such as chain of thought(CoT) of reliance on reasoning training data. The core idea is to create models that can learn from multiple reasoning paths for a single questions.