I think of an llm as a differentiable interpreter of a program. it should do decision making (tool selection, argument routing), branching logic via weights + gates etc.
so as a differentiable state machine:
- each state == a stage in your workflow
- transitions == tool calls
- encode this as a rnn or graph
and learn transitions and actions via supervision or RL
I think of an llm as a differentiable interpreter of a program. it should do decision making (tool selection, argument routing), branching logic via weights + gates etc.
so as a differentiable state machine:
- each state == a stage in your workflow
- transitions == tool calls
- encode this as a rnn or graph
and learn transitions and actions via supervision or RL