[HN Gopher] Optimizing Tool Selection for LLM Workflows with Dif...
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       Optimizing Tool Selection for LLM Workflows with Differentiable
       Programming
        
       Author : viksit
       Score  : 24 points
       Date   : 2025-07-05 20:52 UTC (2 hours ago)
        
 (HTM) web link (viksit.substack.com)
 (TXT) w3m dump (viksit.substack.com)
        
       | viksit wrote:
       | I was experimenting with how local, learnable routers can reduce
       | token overhead, and lower costs, and decided to publish a post
       | about it. The main goal is to delegate tool calls via a PyTorch
       | based learner and examples of how to integrate this into a DSPy
       | pipeline. Feedback welcome!
        
         | krohling wrote:
         | I think this is a creative approach. I wonder how the success
         | rates for that little RNN compare to the success rates of the
         | primary LLM, especially for complex queries or complex tool
         | calls. At some point you have to scale that network up large
         | enough to get better results. Eventually you've come back
         | around and you might as well use an LLM. I think a similar
         | approach with potentially better results (depends on the
         | application) could be accomplished by using that same dataset
         | to finetune a small language model. It'd be interesting to see
         | some success rate comparisons.
        
         | joe_the_user wrote:
         | My question is whether you have managed to make this work,
         | perform a specific complex task, in some real world situation.
        
       | Garlef wrote:
       | Is selection really the issue?
       | 
       | You'd still need to figure out what payload to give to the tool
       | based on your context.
       | 
       | But I guess depending on your business case it might be worth it.
       | It's not something I'd do from the beginning, though.
        
       | tomlue wrote:
       | you could also propagate loss into the tools themselves.
        
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       (page generated 2025-07-05 23:00 UTC)