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

submitted 2 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

top 8 comments

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[–] [email protected] 0 points 2 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 2 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.

[–] [email protected] 0 points 2 days ago (1 children)

Damnit, I saw MCTS and thought it'd be something neat, but then it's LLMs because of course every piece of tech news is LLMs.

[–] [email protected] 0 points 2 days ago

Making the LLM use an LLM to figure out what to say almost feels like a pretty good tech news shitpost

[–] [email protected] 0 points 2 days ago (1 children)

overthinking how someone might react to what it could say in multiple branches with growing resource usage

This is it, if they can get it to reliably decide to just not say anything at all in the end then I have been fully replaced madeline-sadeline

[–] [email protected] 0 points 2 days ago

Lol same, what I was thinking while reading the features was "wow, they found a way to simulate masking!"

[–] [email protected] 0 points 2 days ago (1 children)

Interesting. I’m not sophisticated enough to judge this particular implementation but the concept of generating entire conversation trees to judge the quality of an output intrigues me for sure and I’d be interested in reading more about it and any research around it. Got any good links for further reading?

[–] [email protected] 0 points 2 days ago* (last edited 2 days 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:

https://arxiv.org/abs/2503.19309

https://arxiv.org/abs/2505.23229

https://arxiv.org/abs/2504.02426

https://arxiv.org/abs/2504.11009

https://arxiv.org/abs/2502.13428