Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
Researchers introduce Q-ALIGN DT, a machine learning framework that improves return-conditioned supervised learning by aligning return-to-go signals with actual policy performance using Q-value guidance. The method demonstrates superior controllability and generalization across reinforcement learning benchmarks, potentially advancing AI decision-making systems.
Q-ALIGN DT addresses a fundamental limitation in Conditioned Sequence Models (CSMs), which have treated return-to-go metrics as disconnected numerical inputs without ensuring alignment to actual policy performance. This research bridges that gap through Q-function guidance, creating a feedback mechanism that validates whether higher return targets genuinely produce better-performing policies. The approach combines dense guidance from Q-functions with RTG-perturbation techniques during fine-tuning, establishing consistency between input signals and output behavior.
This work builds on the growing field of return-conditioned reinforcement learning, where models learn to generate trajectories matching specified return targets. Previous methods struggled with controllability degradation and poor generalization to unseen tasks. By enforcing Q-value alignment, researchers ensure that the learned policy family maintains structural integrity across different return conditions, enabling precise task control.
The implications extend beyond academic research into practical AI systems requiring reliable control mechanisms. Superior performance on D4RL benchmarks suggests the framework could enhance robustness in real-world applications where policy controllability directly impacts outcomes. The ability to generalize to velocity-tracking tasks where prior methods fail indicates potential breakthroughs in transfer learning and multi-task optimization.
Future development hinges on scaling these techniques to higher-dimensional state spaces and real-world deployment. The theoretical guarantees around near-optimal policy learning at sufficient return levels provide a foundation for further refinement. Integration of Q-ALIGN DT principles into production systems could accelerate adoption of more controllable and reliable AI agents across robotics, autonomous systems, and financial modeling applications.
- →Q-ALIGN DT enforces alignment between return-to-go inputs and actual Q-values, improving policy control consistency
- →Framework achieves superior performance on D4RL benchmarks and generalizes to tasks where previous methods fail
- →Theoretical analysis confirms near-optimal policy learning when return-to-go signals are sufficiently high
- →The method creates structured policy families that maintain precise alignment across different return conditions
- →Results suggest practical improvements for AI systems requiring reliable controllability and task generalization