> Now we don't know if we're generalising or memorising.
The Arc HRM blog post says:
> [...] we set out to verify HRM performance against the ARC-AGI-1 Semi-Private dataset - a hidden, hold-out set of ARC tasks used to verify that solutions are not overfit [...] 32% on ARC-AGI-1 is an impressive score with such a small model. A small drop from HRM's claimed Public Evaluation score (41%) to Semi-Private is expected. ARC-AGI-1's Public and Semi-Private sets have not been difficulty calibrated. The observed drop (-9pp) is on the high side of normal variation. If the model had been overfit to the Public set, Semi-Private performance could have collapsed (e.g., ~10% or less). This was not observed.
The Arc HRM blog post says:
> [...] we set out to verify HRM performance against the ARC-AGI-1 Semi-Private dataset - a hidden, hold-out set of ARC tasks used to verify that solutions are not overfit [...] 32% on ARC-AGI-1 is an impressive score with such a small model. A small drop from HRM's claimed Public Evaluation score (41%) to Semi-Private is expected. ARC-AGI-1's Public and Semi-Private sets have not been difficulty calibrated. The observed drop (-9pp) is on the high side of normal variation. If the model had been overfit to the Public set, Semi-Private performance could have collapsed (e.g., ~10% or less). This was not observed.