MCTS works cleanly for Go because value estimation is concrete and the action space is revisitable enough for visit-count-based exploration bonuses to mean something, but that same PUCT-style heuristic likely doesn't transfer to LLM reasoning where token sequences are too vast and open-ended to revisit.

Eric Jang explains why MCTS doesn't straightforwardly port to LLMs: Go's concrete value estimates and bounded, revisitable action space let PUCT prune the tree effectively, but language's combinatorially vast action space undermines the same exploration heuristic. ✦ AI generated

Eric Jang · Dwarkesh Podcast · 2026-05-15 · original ↗

But there are two things that make MCTS very simple for Go. Value estimation is concrete. You can determine it for real, and then you can use it to truncate depth, as you said. The breadth is also determined. What’s critical is that the action selection algorithm, where you iteratively visit and grow the tree, is well suited for the size and depth of problem that Go is.

verbatim transcript · starts at 105:47