Reasoning with Exploration: An Entropy Perspective
Adding a single entropy term to the RL advantage function lifts PassK scores dramatically, especially for very large K.
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Adding a single entropy term to the RL advantage function lifts PassK scores dramatically, especially for very large K.
Trace2Skill converts parallel execution trajectories into a shared skill library that works across model sizes and domains.
A rule‑based RL framework lifts multimodal LLM accuracy on GUI tasks by up to 18 percent.
A new spiking transformer backbone removes integer‑float multiplications from residual paths while keeping global attention.
A new reward framework improves preference prediction by 17.2% and raises text‑to‑video win rates by over 30%.
A new dataset of 1.5 M tool‑agentic trajectories, built on authentic Model Context Protocols, pushes open‑source LLM agents toward real‑world complexity.
Large Language Models (LLMs) often exhibit sycophantic behavior, agreeing with user-stated opinions even when those contradict factual knowledge. While prior work has documented this tendency, the internal mechanisms that enable such behavior remain poorly understood.
MoMo‑VLA cuts inference time by more than a third while raising success rates on ten manipulation tasks.
The new MMAU-Pro suite tests AI on 5,305 audio clips across 49 distinct skills.
PRISM slashes GPU time by roughly 70% and pushes accuracy to 101.7% of the full‑data baseline without any extra training.