LRFeed: Latent Residual Feedback for Efficient Reasoning in Looped Transformers

Looped Transformers have emerged as a promising class of models for solving complex reasoning tasks. However, maintaining effective computation over long reasoning horizons remains challenging for these models. To tackle this problem, we introduce **Latent Residual Feedback (LRFeed)**, which captures how the model's internal latent state evolves and feeds this information back to guide subsequent computation. This feedback helps provide awareness of the reasoning process and enhance reasoning efficiency. We instantiate LRFeed on a single-state variant of the Tiny Recursive Model (TRM), retaining its shared Transformer backbone. Ablation studies show that LRFeed strengthens long-horizon reasoning, even beyond the training horizon. It also promotes more diverse internal reasoning dynamics, reflected in a higher effective rank of channel activity across iterations. Across Sudoku-Extreme, Maze-Hard, and ARC-AGI-1, LRFeed surpasses TRM using less than one-eighth of its inference compute. On Sudoku, LRFeed reaches 97.07% accuracy with extended inference, exceeding TRM by 16.22 percentage points at matched compute. LRFeed also offers a favorable accuracy–compute trade-off against other compact looped Transformers.