CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflicts

Researchers propose CARE-RL, a reinforcement learning framework that combines protocol-aware reward generation with capability-aware optimization to address challenges in multi-domain RL systems. The approach achieves improved performance across math, chat, and instruction-following tasks on multiple LLM models, demonstrating advances in making RL more effective across diverse domains.

CARE-RL addresses a fundamental challenge in modern machine learning: extending reinforcement learning across multiple domains without performance degradation. Traditional RL systems struggle when applied to non-verifiable tasks and face capability interference when optimizing across different domains simultaneously. This research tackles both problems through dual innovations.

The Protocol-Aware Generative Reward Model (PA-GRM) solves the reward reliability problem by establishing explicit evaluation protocols before generating rewards. Rather than applying generic reward signals, PA-GRM creates task-specific schemas that enable consistent evaluation of open-ended responses—a critical requirement for domains like creative writing or conversation where "correct" answers don't exist. This approach moves beyond simple scoring metrics toward protocol-driven evaluation.

The second innovation, Direction-Aware Capability Subspace Projection (DACSP), manages cross-domain conflicts by learning from historical optimization patterns. By analyzing which capability directions worked in previous domains, DACSP amplifies beneficial updates while suppressing conflicting ones. This maintains backward compatibility while enabling forward progress—a principle increasingly important as AI systems handle multiple specialized tasks.

The experimental results demonstrate concrete improvements: scores of 47.9 and 50.7 on different model architectures across diverse benchmarks. These gains suggest the framework successfully reduces the performance tradeoffs typically associated with multi-domain learning. For AI developers building systems that must excel at math, language generation, and instruction-following simultaneously, this represents practical progress toward unified, capable models.

• →CARE-RL combines protocol-aware rewards and capability-aware optimization to improve multi-domain reinforcement learning performance
• →PA-GRM establishes evaluation protocols before generating rewards, enabling consistent assessment of open-ended tasks
• →DACSP extracts historical capability directions to amplify beneficial updates while suppressing conflicting ones across domains
• →Framework achieves measurable improvements across math, chat, and instruction-following benchmarks on multiple LLM models
• →Addresses fundamental challenge of extending RL to non-verifiable tasks where traditional reward signals prove unreliable