Principle:Iamhankai Forest of Thought Argument Parsing
| Knowledge Sources | |
|---|---|
| Domains | Configuration, CLI |
| Last Updated | 2026-02-14 03:00 GMT |
Overview
A configuration pattern that parses command-line arguments into a structured namespace for controlling experiment hyperparameters and execution modes.
Description
Argument Parsing is the standard approach for configuring machine learning experiments via command-line interfaces. Rather than hardcoding hyperparameters, experiments accept structured arguments (tree count, iteration depth, model paths, dataset selection, stopping strategy) that allow systematic ablation studies and reproducible runs. In the Forest-of-Thought framework, argument parsing controls critical parameters like the number of trees in the forest, the base reasoning mode (MCTS/CoT/ToT), and the consensus stopping strategy (CGDM/majority/random/score).
Usage
Use this principle when launching FoT experiments from shell scripts or command line. Argument parsing is the entry point for both benchmark evaluation (GSM8K, MATH500, AIME) and Game24 solving workflows. It ensures all hyperparameters are explicitly specified and logged.
Theoretical Basis
Argument parsing follows the separation of concerns principle: experiment logic is decoupled from configuration. Key design choices:
- Typed parameters: Each argument has a declared type (int, str, bool) with defaults
- Constrained choices: Critical parameters like base_mode and stop use enumerated choices to prevent invalid configurations
- Range slicing: start_id/end_id enable parallel processing of dataset subsets