  We present THOUGHTSCULPT, a general reasoning and search method for tasks
with outputs that can be decomposed into components. THOUGHTSCULPT explores a
search tree of potential solutions using Monte Carlo Tree Search (MCTS),
building solutions one action at a time and evaluating according to any
domain-specific heuristic, which in practice is often simply an LLM evaluator.
Critically, our action space includes revision actions: THOUGHTSCULPT may
choose to revise part of its previous output rather than continuing to build
the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art
reasoning methods across three challenging tasks: Story Outline Improvement (up
to +30% interestingness), Mini-Crosswords Solving (up to +16% word success
rate), and Constrained Generation (up to +10% concept coverage).
