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Adversaries can exploit structural vulnerabilities in function-calling LLMs to bypass safety measures, achieving high success rates with minimal effort.
ISPO reduces critical reasoning failures in RLVR by transforming reward structures, leading to superior performance on complex reasoning tasks.
OPRD closes the performance gap between student and teacher models while training 1.44x faster and using 54% less memory than traditional methods.
SkillComposer enables language models to self-evolve skills in real-time, achieving up to +4.5 improvements on agent tasks compared to larger models.
Automating scientific instruments is now as easy as mimicking a human using the GUI, unlocking faster and more reproducible research.
Smaller reasoning models can achieve both higher accuracy and shorter reasoning chains by adaptively penalizing unnecessary reflections and coordinating length penalties with problem complexity.
