CLM-8B caches agent actions to decide faster | VentureBeat

AI agents often use LLMs to choose between a fixed set of tools or rank candidate outputs. In each case, the model processes a prompt and generates tokens even though the application only needs a bounded decision.

Researchers from Stanford and Nvidia have released Contrastive Language Models (CLM), a new approach designed for these kinds of decisions. Instead of generating sequences of tokens, their CLM-8B model creates representations of the current state and the available actions, then selects the action that best matches the state.

This design can be especially useful in applications where agents often make the same types of decisions repeatedly across a workflow. CLM can cache reusable action representations and avoid recomputing them for every request.

In the team’s zero-shot tests across computer use, gaming and tool calling, CLM-8B ran up to 9x faster than TypeSafe’s Jev and matched its success rate on two game tasks. It gave up some accuracy on the other two: it scored 95.2% to Jev’s 99.2% on the BFCL v4 tool-calling benchmark and finished 26 of 30 WikiRacing tasks to Jev’s 30. The largest speedups appeared when the model could reuse actions across many states or choose from a large set of candidates.

CLM also arrives amid growing interest in what TypeSafe calls “System One” models, a category it introduced with Jev earlier this month. CLM adds a contrastive approach to this emerging stack.

How CLM turns decisions into a matching problem

CLM is composed of…