Demystifying Design Choices of Reinforcement Fine-tuning: A Batched Contextual Bandit Learning Perspective

TL;DR

This paper investigates the impact of various design choices in reinforcement fine-tuning by using a batched contextual bandit framework, revealing which factors are most critical for learning and generalization.

Contribution

It introduces a minimalist baseline and an experimental pipeline to disentangle and evaluate the effects of different design choices in reinforcement fine-tuning.

Findings

Identifies critical design choices affecting learning dynamics

Provides insights into the role of advantage and rollout numbers

Establishes a principled experimental framework for analysis

Abstract

The reinforcement fine-tuning area is undergoing an explosion papers largely on optimizing design choices. Though performance gains are often claimed, inconsistent conclusions also arise from time to time, making the progress illusive. Reflecting on this illusion, we still lack principled answers to two fundamental questions: 1) what is the role of each design choice? 2) which ones are critical? This paper aims to shed light on them. The underlying challenge is that design choices are entangled together, making their contribution to learning and generalization difficult to attribute. To address this challenge, we first construct a minimalist baseline for disentangling factors: one rollout per query in each round, the outcome reward serving as the training signal without any advantage trick, and a batch size of thirty-two. This baseline connects to batched contextual bandit learning, which facilitates experimental analysis. Centering around this baseline, we design an experiment pipeline, examining the marginal gains of factors like advantage, number of rollouts, etc. Experiments on three base models and two datasets, not only reveal new understanding on the role of various design choices on learning and generalization dynamics, but also identify critical ones that deserve more effort.