Long-WAM: Scaling the Context of World-Action Models
Long-WAM raises robot task success by scaling causal world-action context with autoregressive video pretraining.
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Long-WAM raises robot task success by scaling causal world-action context with autoregressive video pretraining.
An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.
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