# Selection-based Structured Reasoning

The work introduces Selection-based Structured Reasoning (SSR), which reformulates the free-form reasoning that multimodal agents generate before each action as a selection over pre-specified, reusable natural-language reasoning candidates scored by their likelihood given the current context, without an auxiliary task head, using teacher-forced prefilling and a shared context KV cache for parallel scoring; on seven multimodal search benchmarks with 2B and 4B models, across multiple reinforcement learning objectives and supervised fine-tuning, SSR reaches an average success rate competitive with leading search agents of the same scale while reducing per-turn reasoning latency by over 90% and total per-question model inference latency by 28-54%.

The work introduces Selection-based Structured Reasoning (SSR), which reformulates the free-form reasoning that multimodal agents generate before each action as a selection over pre-specified, reusable natural-language reasoning candidates scored by their likelihood given the current context, without an auxiliary task head, using teacher-forced prefilling and a shared context KV cache for parallel scoring; on seven multimodal search benchmarks with 2B and 4B models, across multiple reinforcement learning objectives and supervised fine-tuning, SSR reaches an average success rate competitive with leading search agents of the same scale while reducing per-turn reasoning latency by over 90% and total per-question model inference latency by 28-54%.