DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
arXiv:2605.31455v1 Announce Type: new Abstract: Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight feedback. Unfortunately, optimizing such behavior presents a sharp dilemma in practice: online reinforcement learning is able to effectively address multi-turn dynamics but is prohibitively expensive due to the cost of generating full correction trajectories at every update, whereas offline supervised fine-tuning (SFT) is efficient but suffers from distribution shift and behavioral collapse. To this en
The proliferation of large language models in interactive settings necessitates more efficient and robust optimization techniques to overcome the limitations of current methods.
This development addresses a critical challenge in advancing AI agent capabilities by proposing a method for efficient multi-turn optimization, which is key for real-world deployment.
The proposed DRIFT method aims to bridge the gap between expensive online reinforcement learning and efficient but limited offline supervised fine-tuning for interactive AI.
- · AI model developers
- · Companies deploying interactive AI assistants
- · Reinforcement learning researchers
- · Inefficient AI training methods
- · Organizations reliant on prohibitively expensive multi-turn optimization
Improved efficiency and performance of multi-turn interactive large language models.
Accelerated development of more sophisticated and robust AI agents capable of complex interactions.
Broader and more effective integration of AI agents across various industries, replacing repetitive human tasks.
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.LG