Public articles linked to the same research event.
arXiv The authors define five requirements for latent reasoning (useful, diverse, explainable, refinable, efficient) and present Flow-based Latent Reasoning (FLaRe), a recipe built on flow matching in a learned latent space: train a VAE on symbolic CoTs, corrupt inputs and latent codes, train the flow mostly near pure noise, let the answer reader see both noised codes and the model's own predicted endpoints, and self-train on verified rollouts; probes show FLaRe improves on Coconut, CODI and PCCoT on all five requirements, compares favorably on arithmetic benchmarks, and reaches 97% of explicit CoT accuracy at about a quarter of its latency.
The authors define five requirements for latent reasoning (useful, diverse, explainable, refinable, efficient) and present Flow-based Latent Reasoning (FLaRe), a recipe built on flow matching in a learned latent space: train a VAE on symbolic CoTs, corrupt inputs and latent codes, train the flow mostly near pure noise, let the answer reader see both noised codes and the model's own predicted endpoints, and self-train on verified rollouts; probes show FLaRe improves on Coconut, CODI and PCCoT on all five requirements, compares favorably on arithmetic benchmarks, and reaches 97% of explicit CoT accuracy at about a quarter of its latency.
The authors define five requirements for latent reasoning (useful, diverse, explainable, refinable, efficient) and present Flow-based Latent Reasoning (FLaRe), a recipe built on flow matching in a learned latent space: train a VAE on symbolic CoTs, corrupt inputs and latent codes, train the flow mostly near pure noise, let the answer reader see both noised codes and the model's own predicted endpoints, and self-train on verified rollouts; probes show FLaRe improves on Coconut, CODI and PCCoT on all five requirements, compares favorably on arithmetic benchmarks, and reaches 97% of explicit CoT accuracy at about a quarter of its latency.
The authors define five requirements for latent reasoning (useful, diverse, explainable, refinable, efficient) and present Flow-based Latent Reasoning (FLaRe), a recipe built on flow matching in a learned latent space: train a VAE on symbolic CoTs, corrupt inputs and latent codes, train the flow mostly near pure noise, let the answer reader see both noised codes and the model's own predicted endpoints, and self-train on verified rollouts; probes show FLaRe improves on Coconut, CODI and PCCoT on all five requirements, compares favorably on arithmetic benchmarks, and reaches 97% of explicit CoT accuracy at about a quarter of its latency.