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

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TL;DR - This study finds that reinforcement learning produces specialized LLMs that retain more performance when merged than supervised fine-tuned models. It attributes this advantage to RL updates that cause less parameter interference across tasks.

Evaluations span five representative tasks and compare RL-trained models with SFT-trained counterparts.
On-policy RL data yields smaller gradient updates, reducing the risk of overwriting knowledge from other tasks.
As RL converges, its objective progressively reduces the magnitude and number of conflicting parameter updates.
Joint optimization of positive and negative examples directs models toward less biased task-specific parameter subspaces.

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

arXiv cs.CL Zixuan Ren, Jinliang Lu, Junhong Wu, Yang Zhao, Dai Dai, Hua Wu, Haifeng Wang, Chengqing Zong 2026-07-24 arXiv:2607.22039

Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0

Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-07-31 06:59:39.312436 UTC

Evaluations span five representative tasks and compare RL-trained models with SFT-trained counterparts.
On-policy RL data yields smaller gradient updates, reducing the risk of overwriting knowledge from other tasks.
As RL converges, its objective progressively reduces the magnitude and number of conflicting parameter updates.
Joint optimization of positive and negative examples directs models toward less biased task-specific parameter subspaces.

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