Meta and Carnegie Mellon RL-trained 8B Qwen3 beats 402B Maverick on tool picking
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.
