Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
arXiv:2605.25443v1 Announce Type: new Abstract: Post-training has significantly enhanced the reasoning capability of Large Reasoning Models (LRMs), especially with Reinforcement Learning (RL) like Group Relative Policy Optimization (GRPO). However, GRPO-style RL methods in multi-domain settings often fail to achieve consistent improvements across all domains due to inherent interference in policy optimization. Prior studies on multi-domain RL primarily focus on alleviating cross-domain interference, while often neglecting the pivotal role of knowledge sharing, which we argue is the key to tran
The continuous drive to improve AI reasoning capabilities and unlock multi-domain performance in large models is paramount for the next generation of AI applications, making breakthroughs in policy optimization highly relevant.
This research provides a method to significantly enhance the multi-domain reasoning capabilities of Large Reasoning Models, overcoming current limitations and enabling broader, more consistent application of sophisticated AI.
The ability of powerful AI models to generalize and perform consistently across diverse tasks without performance degradation in specific domains is improved, leading to more robust and versatile AI systems.
- · AI developers
- · Enterprises deploying multi-task AI
- · Research institutions
- · AI agents sector
- · AI models with limited multi-domain generalization
- · Current GRPO-style RL methods applied verbatim to multi-domain settings
Large Reasoning Models achieve more stable and higher performance across a wider range of applications.
This leads to faster development and deployment of advanced AI agents capable of handling complex, real-world multi-domain problems.
More capable and generalizable AI could accelerate scientific discovery and automate processes across various industries more effectively.
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