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submitted 2 weeks ago by yogthos@lemmygrad.ml to c/technology@hexbear.net

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Instead of just generating the next response, it simulates entire conversation trees to find paths that achieve long-term goals.

How it works:

Generates multiple response candidates at each conversation state
Simulates how conversations might unfold down each branch (using the LLM to predict user responses)
Scores each trajectory on metrics like empathy, goal achievement, coherence
Uses MCTS with UCB1 to efficiently explore the most promising paths
Selects the response that leads to the best expected outcome

Limitations:

Scoring is done by the same LLM that generates responses
Branch pruning is naive - just threshold-based instead of something smarter like progressive widening
Memory usage grows with tree size, there currently no node recycling

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[–] yogthos@lemmygrad.ml 2 points 2 weeks ago* (last edited 2 weeks ago)

I think that's an interesting approach as well. There are a bunch of research papers on using MCTS with LLMs, a few examples here: