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An Open Source Conversation Response Path Exploration System using Monte Carlo Tree Search (github.com)

submitted 5 days ago by [email protected] to c/[email protected]

8 comments fedilink hide all child comments

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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[–] [email protected] 0 points 4 days ago (1 children)

They have finally done it: they've figured out a way to make LLMs even heavier computation-wise amerikkka-clap

[–] [email protected] 0 points 4 days ago

I mean LLMs have gotten orders of magnitude more efficient in just the past year, but also using these types approaches might make it possible to use much smaller models, and iterate on the result.