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

2026-07-27 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

A study systematically explores the impact of training paradigms on large language model (LLM) merging, finding that reinforcement learning (RL)-trained models significantly reduce task conflicts and performance degradation compared to supervised fine-tuning (SFT). Through comprehensive evaluations across five tasks—mathematical reasoning, code generation, instruction following, logical puzzles, and ranking—using LLaMA-3.1-8B as the base model, researchers observed superior performance preservation in RL-trained models after merging. This advantage holds across different merging methods like Averaging, TIEs, and DARE, and various RL algorithms such as GRPO. The superior suitability of RL stems from three factors: smaller gradient updates due to on-policy training data, an optimization objective that attenuates parameter updates as the model converges, and joint optimization of positive and negative examples, which fosters unbiased task-specific parameter subspaces and lower parameter sign conflicts.

Key takeaway

For machine learning engineers building generalist LLMs through model merging, you should prioritize fine-tuning models using reinforcement learning (RL). RL-trained models exhibit significantly less performance degradation and task conflicts post-merging compared to SFT-trained counterparts. This approach allows you to integrate diverse capabilities more effectively, reducing the need for expensive retraining and ensuring more stable, robust merged models.

Key insights

Reinforcement Learning (RL) training inherently reduces task conflicts in LLM model merging, leading to better performance preservation.

Principles

• On-policy data reduces gradient magnitude.
• RL objectives attenuate parameter updates.
• Joint optimization creates unbiased subspaces.

Method

The study systematically compared RL-trained LLMs against SFT-trained LLMs across five tasks and various merging strategies to quantify task conflict mitigation.

In practice

• Prioritize RL fine-tuning for merge-ready LLMs.
• Expect less degradation when merging RL models.
• Analyze parameter sign conflicts for merging compatibility.

Topics

• Large Language Models
• Model Merging
• Reinforcement Learning
• Supervised Fine-Tuning
• Task Conflicts
• LLaMA-3.1-8B

Code references

• OpenRLHF/OpenRLHF
• volcengine/verl

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.