What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling

Excerpt

arXiv:2609.39967v1 Announce Type: new Abstract: Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few parameters and makes them strong on algorithmic tasks. Such compact solvers are natural candidates for tools that an LLM can call on narrow algorithmic subproblems. However, existing models such as HRM, TRM and URM differ in architecture, gradient propagation and training procedure simultaneously. Thi