Rerooting Techniques Enhance Levin Tree Search Scalability
A new arXiv preprint (2605.30664) introduces three rerooter designs for the √LTS algorithm to improve subgoal-based policy tree search. The clustering-based rerooter exploits global state-space structure, the heuristic-based rerooter leverages learned cost-to-go estimates, and a hybrid combines both signals. This framework avoids explicit subgoal reconstruction, enabling scalable search effort allocation with lower overhead. The work overcomes limitations of explicit subgoal generation in complex single-agent deterministic problems.
Key facts
- arXiv:2605.30664v1
- Announce Type: new
- Abstract: Subgoal-based policy tree search uses a policy to guide search
- Effective for complex single-agent deterministic problems
- Often relies on explicit subgoal generation incurring substantial overhead
- Overcomes limitations using a learned rerooter through √LTS algorithm
- Rerooter implicitly decomposes problem into soft subtasks
- Previous work focused on formal guarantees for given or handcrafted rerooters
- Proposes three rerooter designs: clustering-based, heuristic-based, hybrid
- Clustering-based rerooter exploits global state-space structure
- Heuristic-based rerooter leverages learned cost-to-go estimates
- Hybrid combines both signals
- Framework avoids explicit reconstruction and reasoning over generated subgoals
- Enables scalable allocation of search effort with significantly lower overhead
Entities
Institutions
- arXiv