LiTS: A Modular Framework for LLM Tree Search
arXiv:2603.00631v1 Announce Type: new Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. A decorator-based registry enables domain experts to extend to new domains by registering components, and algorithmic researchers to implement custom search algorithms. We demonstrate composability on MATH500 (language reasoning), Crosswords (environment planning), and MapEval (tool use), showing that components and algorithms are orthogonal: components are reusable across algorithms within each task type, and algorithms work across all components and domains. We also report a mode-collapse finding: in infinite action spaces, LLM policy diversity (not reward quality) is the bottleneck for effective tree search. A demonstration video is available at https://youtu.be/nRGX43YrR3I. The package is released under the Apache 2.0 license a
arXiv:2603.00631v1 Announce Type: new Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. A decorator-based registry enables domain experts to extend to new domains by registering components, and algorithmic researchers to implement custom search algorithms. We demonstrate composability on MATH500 (language reasoning), Crosswords (environment planning), and MapEval (tool use), showing that components and algorithms are orthogonal: components are reusable across algorithms within each task type, and algorithms work across all components and domains. We also report a mode-collapse finding: in infinite action spaces, LLM policy diversity (not reward quality) is the bottleneck for effective tree search. A demonstration video is available at https://youtu.be/nRGX43YrR3I. The package is released under the Apache 2.0 license at https://github.com/xinzhel/lits-llm, including installation instructions and runnable examples that enable users to reproduce the demonstrated workflows.
Executive Summary
LiTS is a modular Python framework for Large Language Model (LLM) reasoning via tree search. It decomposes tree search into reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. LiTS enables domain experts to extend to new domains and algorithmic researchers to implement custom search algorithms, showcasing composable components and algorithms. However, a mode-collapse finding reveals LLM policy diversity as the bottleneck for effective tree search in infinite action spaces. The LiTS framework is released under the Apache 2.0 license and includes installation instructions and runnable examples.
Key Points
- ▸ LiTS decomposes tree search into reusable components (Policy, Transition, and RewardModel).
- ▸ The framework enables composable components and algorithms across domains and search algorithms.
- ▸ A mode-collapse finding reveals LLM policy diversity as the bottleneck for effective tree search in infinite action spaces.
Merits
Modularity
LiTS's modular design enables domain experts to extend to new domains and algorithmic researchers to implement custom search algorithms, promoting composability and reusability.
Scalability
The framework's decomposed components facilitate easier handling of complex tasks and large action spaces, enhancing scalability and flexibility.
Demerits
Mode-Collapse Issues
LiTS may suffer from mode-collapse in infinite action spaces, where LLM policy diversity is the bottleneck for effective tree search, potentially limiting its performance in certain scenarios.
Implementation Complexity
The framework's modularity and customizability may introduce implementation complexity, requiring users to have a solid understanding of tree search algorithms and LLM reasoning.
Expert Commentary
LiTS is a significant contribution to the field of LLM reasoning, offering a modular and composable framework for tree search. While it demonstrates impressive scalability and flexibility, the mode-collapse finding highlights the need for further research in addressing this limitation. As the AI landscape continues to evolve, the development of frameworks like LiTS will play a crucial role in advancing the capabilities of LLMs. Nevertheless, the implementation complexity and potential mode-collapse issues require careful consideration and attention from developers and researchers.
Recommendations
- ✓ Developers and researchers should carefully evaluate the performance of LiTS in various domains and tasks to ensure its effectiveness and scalability.
- ✓ Future research should focus on addressing the mode-collapse issue and exploring new techniques to improve LLM policy diversity in infinite action spaces.