Generative Recursive Reasoning

How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative to autoregressive sequence extension by performing iterative latent-state refinement with shared transition functions. Yet existing RRMs are largely deterministic, following a single latent trajectory and converging to a single prediction. We introduce Generative Recursive reAsoning Models (GRAM), a framework that turns recursive latent reasoning into probabilistic multi-trajectory computation. GRAM models reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and inference-time scaling through both recursive depth and parallel trajectory sampling. This yields a latent-variable generative model supporting conditional reasoning via pθ(y | x) and, with fixed or absent inputs, unconditional generation via pθ(x). Trained with amortized variational inference, GRAM improves over deterministic recurrent and recursive baselines on structured reasoning and multi-solution constraint satisfaction tasks, while demonstrating an unconditional generation capability.

A central question for future neural reasoning systems is how extended computation should be implemented. Large autoregressive models typically scale reasoning by extending a sequence-generation process, whether intermediate computation is expressed explicitly as chain-of-thought tokens or implicitly in hidden or latent representations. A complementary direction is explored by Recursive Reasoning Models (RRMs), which use repeated computation to refine a persistent latent state rather than to append new elements to an output or reasoning sequence. This approach is appealing because it decouples reasoning depth from both parameter scale and output length: a compact model can perform many steps of internal computation by repeatedly applying shared transition functions over time.

While recurrent latent-state refinement provides an appealing mechanism for efficiently increasing reasoning depth, depth alone is not sufficient for many reasoning problems. A capable reasoning system should also be able to maintain uncertainty, consider alternative hypotheses, and explore multiple possible solution strategies. This is especially important in settings where ambiguity or multiple valid solutions are intrinsic, and more generally in problems where a single refinement path may become trapped in a suboptimal reasoning trajectory. In this sense, future RRMs should be not only deep, in the sense of repeated refinement, but also wide, in the sense of maintaining and exploring multiple latent trajectories in parallel.

We introduced GRAM, a generative framework that transforms deterministic recursive architectures into probabilistic generative models capable of modeling both p(y | x) and p(x) via recursive amortized variational inference. For reasoning problems, introducing stochasticity into latent transitions enables diverse solution discovery and improved exploration compared to deterministic counterparts. Notably, we demonstrate GRAM can leverage width-based inference-time scaling as a complement to depth: by sampling multiple latent trajectories in parallel, bypassing the latency bottleneck of depth-only scaling. Our ablations further reveal that stochastic guidance is a general-purpose extension that consistently improves any recursive architecture, and that the gains stem specifically from the variational framework — not from mere randomness, as naive stochastic alternatives applied to existing models yield no improvement.