Test-time Adaptation of Tiny Recursive Models
This work addresses compute constraints for participants in the ARC Prize competition, but it is incremental as it builds on existing tiny recursive models with a focus on efficient adaptation.
The paper tackled the problem of adapting tiny recursive models to competition tasks within strict compute limits by pre-training on public ARC tasks and then fine-tuning, achieving a score of 6.67% on semi-private evaluation tasks.
Prior to the close of the 2025 ARC Prize competition, the leading open source approach - known as TRM, or Tiny Recursive Models - involved training a 7M parameter recursive neural network on augmented variants of ARC tasks. That approach scored approximately 7.8% on the public ARC AGI II evaluation set, but required a level of compute far in excess of what is allowed during the competition. This paper shows that, by starting from a tiny recursive model that has been pre-trained on public ARC tasks, one can efficiently fine-tune on competition tasks within the allowed compute limits. Specifically, a model was pre-trained on 1,280 public tasks for 700k+ optimizer steps over 48 hours on 4xH100 SXM GPUs to obtain a ~10% score on the public evaluation set. That model was then post-trained in just 12,500 gradient steps during the competition to reach a score of 6.67% on semi-private evaluation tasks. Notably, such post-training performance is achieved by full-fine tuning of the tiny model, not LoRA fine-tuning or fine-tuning of task embeddings alone.