tutorial#

ReasoningAgent Update - Beam Search, MCTS, and LATS for LLM Reasoning

Key Updates in this Release:

1. Configuration Changes
2. All reasoning parameters are now configured through a single reason_config dictionary

3. Breaking Change: Parameters like max_depth, beam_size, and answer_approach have moved from constructor arguments into reason_config

4. New Search Strategies

5. Added Monte Carlo Tree Search (MCTS) as an alternative to Beam Search

6. Introduced Language Agent Tree Search (LATS) - an enhancement to MCTS that incorporates reflection prior to the next round of simulation.

7. Enhanced Features

8. New forest_size parameter enables maintaining multiple independent reasoning trees
9. Support for ground truth answers in prompts to generate training data for LLM fine-tuning

Introduction

In our previous post, we introduced the ReasoningAgent, which utilized Beam Search for systematic reasoning. Today, we include MCTS (Monte Carlo Tree Search) and Language Agent Tree Search (LATS) as alternative search strategies, which present advantages in different scenarios.

Our previous ReasoningAgent draws inspiration from OpenAI's 2023 paper, Let's Verify Step by Step, as well as the 2024 O1 feature. The landscape of contemporary research is rich, with notable works such as DeepSeek-R1, Macro-O1, and OpenR.