Behavior Injection: Preparing Language Models for Reinforcement Learning

• URL: http://arxiv.org/abs/2505.18917v1
• Date: Sun, 25 May 2025 00:54:50 GMT
• Title: Behavior Injection: Preparing Language Models for Reinforcement Learning
• Authors: Zhepeng Cen, Yihang Yao, William Han, Zuxin Liu, Ding Zhao,
• Abstract summary: Reinforcement fine-tuning (RFT) has emerged as a powerful post-training technique to incentivize the reasoning ability of large language models (LLMs)<n>LLMs can respond very inconsistently to RFT: some show substantial performance gains, while others plateau or even degrade.<n>We propose behavior injection, a task-agnostic data-augmentation scheme applied prior to RL.
• Score: 24.46625106928253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement fine-tuning (RFT) has emerged as a powerful post-training technique to incentivize the reasoning ability of large language models (LLMs). However, LLMs can respond very inconsistently to RFT: some show substantial performance gains, while others plateau or even degrade. To understand this divergence, we analyze the per-step influence of the RL objective and identify two key conditions for effective post-training: (1) RL-informative rollout accuracy, and (2) strong data co-influence, which quantifies how much the training data affects performance on other samples. Guided by these insights, we propose behavior injection, a task-agnostic data-augmentation scheme applied prior to RL. Behavior injection enriches the supervised finetuning (SFT) data by seeding exploratory and exploitative behaviors, effectively making the model more RL-ready. We evaluate our method across two reasoning benchmarks with multiple base models. The results demonstrate that our theoretically motivated augmentation can significantly increases the performance gain from RFT over the pre-RL model.

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