Tag
VisualPatchWorld introduces a method for learning world dynamics as code, enabling inspectable and editable simulators from data. It achieves strong planning success in navigation and manipulation tasks.
This paper introduces C-MTP, a direct supervision method for training continuous chain-of-thought models that compresses reasoning traces into latent representations. The method performs competitively on simple tasks but reveals that both direct and indirect supervision methods struggle with complex long reasoning traces, showing about 65% performance drop.
This paper investigates how much structure a task needs from a world model, showing that the objective's dimensionality determines how many predictive directions the model installs, with the common scalar reward objective being only the rank-one corner of value equivalence.
This thread presents a theoretical result showing that predicting abstract latent representations (as in JEPA and data2vec) instead of raw tokens can exponentially reduce the data gap between AI and human learning.
This paper introduces Learned Relay Representations (Relay), a method that allows masked diffusion models to propagate latent information across denoising steps, overcoming the hard reset problem and improving performance-latency trade-offs. The method is shown to outperform standard supervised finetuning on coding tasks while reducing inference latency by up to 32%.
Sub-JEPA improves LeWorldModel by applying Gaussian regularization in frozen random orthogonal subspaces, consistently outperforming the original on benchmarks with up to +10.7 percentage points improvement.
Introduces Next-Latent Prediction (NextLat), a self-supervised objective that trains transformers to predict their next latent state, encouraging compact internal world models and improving generalization across sequence modeling tasks.