Principle:Princeton nlp Tree of thought llm Naive Baseline Sampling
| Knowledge Sources | |
|---|---|
| Domains | LLM_Reasoning, Evaluation, NLP |
| Last Updated | 2026-02-14 03:30 GMT |
Overview
A direct sampling strategy that generates multiple independent LLM completions in a single pass without tree search, serving as the baseline for evaluating Tree of Thoughts improvements.
Description
Naive Baseline Sampling produces solution candidates by asking the LLM to solve the problem directly, either via a standard input-output prompt (IO baseline) or via a Chain-of-Thought prompt (CoT baseline). Unlike BFS Tree Search, there is no iterative generation-evaluation-selection loop—all candidates are generated in one call with the n parameter, making each completion independent of the others.
This baseline is critical for measuring the added value of deliberate tree search. The paper shows that for Game of 24, IO achieves 7.3% and CoT achieves 4.0%, compared to ToT BFS at 74%, demonstrating the benefit of search over single-pass generation.
Usage
Use this principle when establishing performance baselines for a task before running the full ToT search. It is activated by the --naive_run flag in the experiment CLI and serves as the control condition in experimental comparisons.
Theoretical Basis
Naive sampling generates n solutions independently:
# Abstract: single-pass generation (no tree search)
prompt = format_prompt(task, input, method='standard') # or 'cot'
solutions = llm(prompt, n=n_samples) # all independent
# No evaluation, no selection, no iterative refinement
The two baseline variants are:
- IO (Input-Output): Uses a few-shot prompt with input-answer pairs. The LLM maps directly from input to answer.
- CoT (Chain-of-Thought): Uses a few-shot prompt with step-by-step reasoning traces. The LLM generates intermediate steps before the answer.
Both generate all n completions in a single batch, with no opportunity for the model to evaluate or refine its reasoning. This makes them computationally cheaper (one LLM call vs. many) but less capable on tasks requiring deliberate exploration.