Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs

2026-07-24

AI Summary

This study addresses the performance degradation commonly observed when merging multiple specialized large language models due to task interference. The authors systematically compare the impact of reinforcement learning (RL) and supervised fine-tuning (SFT) on model merging effectiveness, revealing for the first time that RL mitigates task conflicts through three key mechanisms: modest gradient updates, reduced parameter updates in conflicting regions, and joint optimization over both positive and negative samples. Experimental results across five representative tasks demonstrate that models trained with RL exhibit significantly less performance degradation after merging and consistently outperform their SFT counterparts. These findings establish RL-based merging as a promising new paradigm for efficient multi-task model integration.

Abstract

Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language models (LLMs). Recent research has primarily focused on developing strategies to enhance merging performance with the trained models, while the impact of training paradigms, such as supervised fine-tuning (SFT) and reinforcement learning (RL), on the effectiveness of model merging remains underexplored. In this study, we systematically explore the merging behavior of RL-trained LLMs compared to those trained with traditional SFT. Through comprehensive evaluations across five representative tasks, we find that RL significantly reduces task conflicts and results in less performance degradation after merging, making RL-trained models particularly well-suited for this process. To unearth the reasons behind the superior suitability of RL for model merging, we conduct extensive empirical experiments and theoretical analyses. Our findings highlight three key factors: (1) On-policy training data in RL control the gradient updates in a smaller magnitude, reducing the risk of overwriting existing knowledge for other tasks in the model. (2) The RL optimization objective, which favors ``\textit{enough is as good as a feast}", progressively reduces the magnitude and the number of conflict parameter updates as the model converges. (3) Joint optimization of positive and negative examples in RL steers the model towards an unbiased task-specific parameter subspace, ensuring robust performance while further preventing parameter conflicts.

Problem

• model merging
• task conflicts
• reinforcement learning
• large language models
• supervised fine-tuning

Innovation

• Reinforcement Learning
• Model Merging
• Task Conflict
• Parameter Subspace
• Gradient Update