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

TL;DR

This work analyzes design choices in reinforcement fine-tuning for LLMs by establishing a minimalist baseline tied to batched contextual bandits to disentangle effects. It introduces an experimental pipeline and evaluates multiple design factors (advantage, rollouts, batch size, replay) across three base models and two math datasets, revealing that many factors yield only modest gains and that some model-dataset pairs exhibit near-zero improvements. Notably, enabling GRPO-style advantages, increasing rollouts, or scaling batch size often provide limited, sometimes diminishing returns, while a replay strategy can approximate optimal tradeoffs. The findings call for systematic, unified benchmarks to understand when and where reinforcement fine-tuning design choices truly improve learning and generalization, guiding more efficient resource allocation in future work.

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.

Figures (14)

• Figure 1: The minimalist baseline on GSM8K dataset.
• Figure 2: The minimalist baseline on MATH dataset.
• Figure 3: Impact of advantage function on GSM8K dataset.
• Figure 4: Impact of advantage function on MATH dataset.
• Figure 5: Impact of scaling rollouts on GSM8K dataset.