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This paper introduces a reinforcement learning method for training a meta-reasoning policy that selects between fast reactive control and slower deliberative planning based on uncertainty in the reactive policy, achieving better balance and adaptivity in navigation tasks.
HALO introduces a hybrid adaptive latent refinement method for frozen language models that selectively applies second-stage refinement to a subset of tokens, achieving better performance than fixed refinement steps while using less compute.
Looped World Models introduce iterative latent state refinement through shared transformer blocks, achieving 100x parameter efficiency while adapting computational depth to prediction complexity.
Proposes AdaSR, a framework enabling reasoning models to process streaming inputs adaptively, and HRPO, a hierarchical reinforcement learning method to optimize thinking allocation for accuracy-efficiency trade-offs.
DeepMind researcher Brendan O'Donoghue provides an in-depth introduction to text diffusion models, which generate text through iterative denoising. Compared to autoregressive models, they offer lower latency but limited throughput, and demonstrate unique advantages such as self-correction and dynamic computation.
Light Interaction introduces a training-free inference acceleration framework for interactive video world models, using adaptive context management, denoising cache acceleration, and 3D block sparse attention to achieve up to 2.59x speedup while maintaining competitive visual quality.
This paper presents Token-Selective Attention (TSA), a differentiable token routing mechanism that learns to skip unnecessary computations per token in transformer layers, reducing token-layer operations by 14–23% with minimal quality loss on language modeling tasks.