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> Most of that additional reasoning is a lot of garbage and you can redirect it to useful output. What do you mean by "redirect it to useful output"? Could you
by nomel 2mo ago
> Most of that additional reasoning is a lot of garbage and you can redirect it to useful output.
What do you mean by "redirect it to useful output"? Could you give an example? This sounds interesting.
- cyanydeez 2mo agoIt's specific to the harness. Using dynamic context pruning, the budget cuts it off after a select amount of tokens and the budget message tells the model to use subgents to finish whatever it's thinking about
- nomel 2mo agoNice. Does it use a summarization, or a hard cutoff?
- ttkciar 2mo agollama.cpp uses a hard cutoff. The agent then does "something" that is specific to the agent's implementation and configuration. It might summarize and then "finish the thought" with a different model, and then resubmit the prompt to the llama.cpp API endpoint with <think>..</think> prefilled. The primary model then infers the remainder of the reply.
- cyanydeez 2mo agollama.cpp does a hard cut off on budget; it can set a reasoning-message as default but the client _can_ set a per message reasoning-message, so it's possible a smart harness could inspect the cut of thoughts and trim and do whatever.