arXiv:2606.02398v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation, question answering, and creative writing (CW), but training on one domain often degrades performance on others. Existing explanations based on catastrophic forgetting or global gradient conflict are incomplete: substantial interference can occur even when full-model gradients are nearly orthogonal. We show that single-domain RL produces sparse, small-magnitude parameter edits with weak overlap am
The rapid development and deployment of LLMs across various applications necessitate deeper understanding and mitigation of training interference as models become more multi-functional.
Improving the ability of RL to enhance LLMs across multiple domains without degrading performance on others is crucial for developing more robust, general-purpose AI and reducing the cost of specialized model development.
This research provides a new theoretical framework for understanding and potentially resolving cross-domain interference in multi-domain reinforcement learning for LLMs.
- · AI developers
- · Companies using multi-domain LLMs
- · Researchers in RL and LLM
- · Companies with siloed AI development
More efficient and capable general-purpose LLMs can be trained with less performance degradation across tasks.
This could accelerate the development of complex AI agents capable of handling diverse responsibilities within a single model.
Improved multi-domain RL could lead to a consolidation of AI models and platforms, reducing the need for numerous specialized models.
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