Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications
arXiv:2606.00838v1 Announce Type: new Abstract: Inductive generalization is a framework for reinforcement learning (RL) generalization in which inductively related task instances admit inductively related policies. Prior work captures this structure via a higher-order policy-evolution function learned directly with RL, but suffers from poor training scalability: as training tasks grow, aggregated reward feedback becomes noisy and conflicting, destabilizing training and weakening generalization. We propose DIBS, a decoupled behavioral cloning approach that separates learning task-specific polic
The increasing complexity and scale of AI models necessitate more efficient and stable training methods to achieve robust generalization in reinforcement learning.
Improved inductive generalization in RL can accelerate the development of more capable and reliable AI agents for diverse applications.
The proposed 'decoupled behavioral cloning' offers a more scalable and stable approach to training RL policies, addressing a key bottleneck in complex AI system development.
- · AI research and development
- · Reinforcement learning applications
- · Robotics and automation
- · AI agent developers
- · Inefficient RL training methods
- · Companies reliant on brute-force RL approaches
More efficient training leads to faster iteration and deployment of advanced AI agents.
The improved generalization capabilities could enable AI agents to tackle a wider range of previously intractable problems in the real world.
This could contribute to the acceleration of AI agent adoption across industries, impacting white-collar workflows and operational efficiency.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI