Beyond a Single Thought: Learning Diverse Latent Reasoning Paths with SoftPaths

Chain-of-Thought (CoT) reasoning improves the problem-solving capabilities of large language models by generating intermediate reasoning steps, but explicitly verbalizing these steps incurs substantial inference costs and often produces redundant content. Latent CoT alleviates this limitation by encoding intermediate reasoning in continuous representations. However, existing methods often rely on a single latent condition or generate multiple variants through simple resampling or perturbation, which may provide limited exploration of the solution space and fail to capture functionally distinct reasoning directions. We propose SoftPaths, a two-stage framework that learns multiple structured latent reasoning paths and adapts the generation policy to reason effectively under these paths. In the first stage, path-specific routers compose a shared set of projection experts to construct multiple input-dependent latent conditions. Answer-only supervision ensures that each path remains predictive of the final answer, while a contrastive diversity objective mitigates representation collapse across paths. In the second stage, the learned latent-path constructor is frozen, and the paths are activated in a round-robin manner to provide diverse latent conditions for GRPO-based policy optimization. A correctness-gated length reward prioritizes answer correctness while discouraging unnecessary verbalization among correct responses. At inference time, SoftPaths supports both efficient single-path reasoning and multi-path test-time scaling through answer aggregation. Experiments across multiple reasoning benchmarks demonstrate that SoftPaths consistently improves the balance between answer accuracy and efficiency over explicit and latent CoT methods.