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