#ToolBench

The result argues that tool selection can be moved off the flagship model entirely: a small RL-trained selector handles catalogs of 10,000+ tools that cannot fit in context, where retrievers alone rank only by relevance. Teams building agents with large tool catalogs can swap in a lightweight selector while keeping their main model unchanged. The performance gap over a prompted 402B model, 41.5% versus 30.6% exact picks, makes the cost saving concrete.

rohanpaul_ai@rohanpaul_aiSource

Small models trained specifically for tool selection can outperform much larger prompted LLMs on the routing step, so agent builders can cut cost and latency by replacing the proprietary LLM in their tool-selection pipeline. The selector's ability to work with different retrievers and unseen domains makes it practical to slot into existing RAG-style stacks. Teams building large-scale agent tooling should compare this approach before defaulting to a frontier model for routing.

DAIR.AI@dair_aiSource