A total of 191,568houses are sampled from this distribution, with ratio of10:1:1 across training, validation, and test.

Using 16-bit mixedprecision training SPOC trains at approximately 1.2 hoursper 1000 iterations

SPOC uses SIGLIP image and text encoders that produce84 × 768 (npatch × dimage) features and 768 dimension fea-ture per text token. We use 3-layer transformer encoder anddecoder with 512 dimensional hidden state (dvisual) and 8attention heads for the goal-conditioned visual encoder andaction decoder, respectively. We use a context window of100. All models are trained with a batch size of 224 on8×A100 GPUs (80 GB memory / GPU) with the AdamWoptimizer and a learning rate of 0.0002. Single-task modelsare trained for 20k iterations, while multi-task models aretrained for 50k iterations.

SPOC uses SIGLIP image andtext encoders. We use 3-layer transformer encoder and de-coder and a context window of 100. All models are trainedwith batch size=224, AdamW and LR=0.0002. Single-taskmodels and multi-task models are trained for 20k and 50kiterations, respectively. Using 16-bit mixed precision train-ing SPOC trains at an FPS of ≈3500, compared to an FPSof ≈175 for RL implemented using AllenAct

To analyze models we first present results on a subset of15 object categories from the full 863 categories, called S.Evaluations on the full category set are named L. Our train-ing data contains an average of 90k episodes per task andon average each task contains 195 episodes in the evalua-tion benchmark.

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