Research
RELOAD: Looping Layers in Typed Decision Models Improves Reasoning and Calibration
RELOAD is a method that retrofits loops into pretrained decision models, allowing them to spend variable test-time compute in latent space without adding parameters. When trained with the model, these loops improve accuracy and calibration, especially on problems requiring longer reasoning chains than seen in training. Across six models from 0.4B to 4B parameters, encoders gain up to 23 points, and a lightweight controller preserves most gains at about three-quarters of the compute.
adaptive computationmodel efficiencyreasoning