Training Language Models to Cooperate with Inference-Time Controllers
Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reasoning. Existing post-training methods, however, typically optimize for a single fixed interaction pattern, despite real deployments relying on diverse controllers such as Chain-of-Thought, self-consistency, debate, planning, and verification pipelines. This creates a training--deployment mismatch and limits transfer to new workflows. We introduce CALM (Controller-Aware Language Models), a post-training framework that explicitly places controllers in the training loop. We formulate controller-aware post-training as multi-task reinforcement l...
| Authors | Moumita Choudhury, Vanshaj Khattar, Jing Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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