RL Objectives Boost Offline In-Context RL Performance by 30%
A recent study published on arXiv (2502.17666) highlights the advantages of incorporating reinforcement learning (RL) objectives within offline in-context reinforcement learning (ICRL) frameworks, leading to notable performance enhancements. The investigation analyzed over 150 datasets from GridWorld and MuJoCo environments, revealing that optimizing RL objectives results in an average performance boost of around 30% when compared to the commonly utilized Algorithm Distillation (AD) technique. This enhancement was consistently observed across diverse dataset coverages, structures, expertise levels, and environmental complexities. In the more demanding XLand-MiniGrid setting, RL objectives resulted in performance that was twice as effective as AD. Furthermore, the study indicated that introducing conservatism during value learning yielded additional benefits in nearly all evaluated scenarios. These results emphasize the necessity of aligning ICRL learning goals with RL principles, advocating for a transition from traditional supervised training to more adaptive RL-based strategies in offline contexts.
Key facts
- Study explores integration of RL objectives in offline ICRL framework
- Tested on over 150 GridWorld and MuJoCo environment-derived datasets
- RL objectives improve performance by ~30% on average vs Algorithm Distillation (AD)
- Improvements observed across dataset coverages, structures, expertise levels, and complexities
- In XLand-MiniGrid, RL objectives doubled AD performance
- Adding conservatism during value learning yields additional improvements
- Findings emphasize aligning ICRL learning objectives with RL principles
- Paper available on arXiv with ID 2502.17666
Entities
Institutions
- arXiv