arXiv:2606.00609v1 Announce Type: new Abstract: Reinforcement learning (RL) with verifiable rewards has achieved strong progress in reasoning-oriented LLMs, but extending it to multi-domain RL remains challenging due to reward unreliability in non-verifiable tasks and capability interference across domains. We propose CARE-RL to combine protocol-aware reward generation with capability-aware optimization for mitigating cross-domain conflicts. For non-verifiable tasks, the Protocol-Aware Generative Reward Model (PA-GRM) constructs prompt-level evaluation protocols and schemas before producing tr
The rapid advancement of LLMs necessitates robust RL methods to overcome limitations in real-world, multi-domain applications.
This research addresses key challenges in scaling AI, particularly in creating reliable and generalizable autonomous systems, which are critical for future AI applications.
The ability to manage cross-domain conflicts and create more reliable reward systems could significantly accelerate the development of advanced AI agents.
- · AI agents developers
- · Robotics industry
- · SaaS providers
- · Research institutions
- · Developers relying on single-domain RL
- · Systems with unreliable reward functions
Improved reliability and generalization of reinforcement learning systems, especially in complex multi-domain environments.
Faster development and deployment of sophisticated AI agents capable of handling diverse and unstructured tasks.
Acceleration of autonomous systems across various industries, leading to significant productivity gains and new service models.
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Read at arXiv cs.LG