Apple, CMU's SSR turns agent reasoning into multiple-choice, cutting per-turn latency 90%
Researchers at Apple and Carnegie Mellon University propose SSR (Selection-Based Structured Reasoning), which replaces each free-form thinking step in an agent loop with a choice from a library of six predefined reasoning options. Scoring reuses the model's own token likelihoods and prefills all options in parallel, so no new tokens are generated autoregressively, with no extra classification head and no SFT warm start. On Qwen3-VL 2B/4B models with H100 + SGLang across seven multimodal search benchmarks (MMSearch, InfoSeek, SimpleVQA, LiveVQA and others), the paper reports per-turn reasoning latency down more than 90% and total per-question latency down 28–54%, while success rates match free-form thinking (4B SSR+GRPO averaging 61.37%, 2B reaching 51.26%). The authors say SSR plugs into existing SFT and RL pipelines and is best understood as reorganizing the reasoning space from open-ended language into a compact, reusable strategy space, noting that open-ended math, theorem proving and code design still need free generation.
It offers a general way to cut agent inference cost with little loss in task success, which matters most for small on-device agents.
AppleCarnegie Mellon UniversitySSR (Selection-Based Structured Reasoning)