[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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