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Staleness-Adaptive Trust Regions reshape update geometry in asynchronous reinforcement learning, achieving record performance while controlling for high-staleness updates.
Behavior localization is revolutionized, enabling developers to seamlessly connect high-level modification requests to specific code locations in complex AI harnesses.
Self-distillation in a verifiable environment enables web agents to achieve competitive performance without reliance on external teacher models.
HiLS-Attention achieves over 64x context length extrapolation with 90% retrieval accuracy, outperforming traditional full attention mechanisms.
FlashMemory-DeepSeek-V4 slashes GPU memory usage by over 90% for ultra-long contexts while enhancing model accuracy.
Learnable critics that evaluate the model's own GUI grounding proposals, rather than relying on static geometric heuristics, unlock substantial gains in accuracy.
