Papers with multiple refinement steps
TRM-Planner: Offline Target Planning and Distillation for Tiny Recursive Models (2026.findings-acl)
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| Challenge: | Tiny Recursive Models (TRMs) perform iterative reasoning with an Adaptive Computation Time (ACT)-style loop, but their supervised training targets can be brittle and their halting behavior difficult to tune. |
| Approach: | They propose a two-stage teacher-cache distillation recipe that shifts compute to offline teacher-caching stage. |
| Outcome: | The proposed model improves su-pervision while leaving student-time inference unchanged. |