{"id":1181778,"date":"2026-07-26T00:00:00","date_gmt":"2026-07-26T07:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=1181778"},"modified":"2026-08-15T11:46:31","modified_gmt":"2026-08-15T18:46:31","slug":"training-language-models-to-cooperate-with-inference-time-controllers","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/training-language-models-to-cooperate-with-inference-time-controllers\/","title":{"rendered":"Training Language Models to Cooperate with Inference-Time Controllers"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">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&#8211;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 learning over controller-induced interaction protocols, where controllers are compositions of reusable local reasoning modules. This structure also induces a module-level decomposition of mixed-controller training under a turn-level GRPO objective, enabling a systematic study of controller and module-aware training strategies. We evaluate CALM on held-out controller compositions and broader controller shifts, showing that controller-aware post-training improves generalization across inference-time workflows beyond single-controller optimization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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&#8211;deployment [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Moumita Choudhury","user_id":0},{"type":"text","value":"Vanshaj Khattar","user_id":0},{"type":"user_nicename","value":"Jing Liu","user_id":"43843"},{"type":"text","value":"T. Koike-Akino","user_id":0},{"type":"text","value":"Ankush Chakrabarty","user_id":0},{"type":"text","value":"S. 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