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Merging large language models just got a lot faster and more efficient, with MergePipe cutting expert-read I/O by up to 90% while preserving performance.
Forget blindly chasing teacher-student disagreement in on-policy distillation – focusing on *learnable* disagreement, where the teacher nudges the student within its existing possibilities, unlocks surprisingly efficient learning.
Maximizing reward entropy by targeting a 50% pass rate in binary-reward RL unlocks significant speedups and performance gains in agentic tasks.
This new OCR model beats Gemini-3.1-Pro and Qwen3-VL-235B on key information extraction, thanks to its clever "Layout-as-Thought" process that recovers layout grounding in end-to-end OCR.
