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A two-stage OPD-then-RL approach outperforms traditional methods by leveraging the strengths of both on-policy distillation and reinforcement learning without the interference seen in joint optimization.
Smaller language models can efficiently replace larger ones in rubric-based reinforcement learning, achieving competitive performance with significantly reduced computational costs.
Skill augmentation can elevate CLI agent performance beyond GUI counterparts, revealing that the true challenge lies in skill coverage rather than model capability.
SPWM cuts computational costs and energy consumption in image restoration tasks while maintaining high image quality, showcasing the untapped potential of spiking neural networks.
MLLMs struggle with visually rendered text not because they can't reason, but because they can't *read* it, and a simple self-distillation fix closes the gap.
