Yes, Q-learning Helps Offline In-Context RL

Researchers demonstrate that integrating reinforcement learning objectives into offline in-context RL frameworks significantly outperforms supervised learning approaches like Algorithm Distillation, achieving ~30% performance improvements across diverse environments and doubling performance in complex settings. The findings validate that aligning ICRL training with RL reward-maximization goals, particularly through conservative value learning, produces more effective agents.

This research addresses a fundamental tension in offline in-context reinforcement learning: the gap between how models are trained and what they're ultimately meant to accomplish. While existing ICRL methods have relied heavily on supervised learning objectives, this work reveals that directly optimizing for RL goals produces substantially better outcomes. The comprehensive evaluation across 150+ datasets demonstrates robustness rather than cherry-picked results, lending credibility to the findings.

The breakthrough emerges from recognizing that supervised objectives, while computationally tractable, don't fully capture the reward-maximization intent underlying reinforcement learning. By introducing RL objectives within offline settings—combined with conservatism constraints to prevent out-of-distribution issues—researchers unlock performance gains that approach 30% improvement. The doubling of performance in XLand-MiniGrid environments suggests that benefits scale with environmental complexity, where reward-aligned training becomes increasingly valuable.

This advancement matters for the broader AI development pipeline. Better offline RL methods accelerate training on historical data without requiring interactive environment exploration, reducing computational costs and enabling faster iteration on complex domains. For developers building RL systems, this validates investing in RL-aligned objectives rather than purely supervised approaches. The conservatism additions also address practical deployment concerns around safety and stability.

The research trajectory points toward more sophisticated hybrid approaches that integrate multiple learning objectives. Future work likely explores adaptive weighting between supervised and RL objectives, context-dependent conservatism levels, and scaling to even larger offline datasets. The validation that offline RL genuinely improves ICRL opens pathways for applying these techniques to real-world robotics and decision-making systems where data collection is expensive.

• →RL objectives outperform supervised learning by ~30% in offline in-context RL across diverse environments
• →Conservative value learning during RL optimization further improves performance across nearly all tested settings
• →Performance gains scale dramatically with environmental complexity, doubling in XLand-MiniGrid over Algorithm Distillation
• →Aligning training objectives with reward-maximization goals produces more effective and practical RL agents
• →Results validated across 150+ GridWorld and MuJoCo datasets, demonstrating robustness across dataset coverage and expertise levels