Tag
reasoning models
Reasoning models are built to handle multi-step inference, verification, and agentic tasks such as math, coding, and interactive problem solving. This tag covers training methods, cold-start behavior, RLVR, loss design, and the cost-performance tradeoffs that shape deployment.
2 articles
Research/Apr 29
Tsallis loss for faster reasoning-model training
A Tsallis-loss continuum may help reasoning models escape cold-start stalls faster than RLVR, with tradeoffs between speed, noise, and stability.
Research/Apr 2
ARC Prize leaderboard shows cost still matters
ARC Prize’s leaderboard tracks how AI systems trade cost for score, and ARC-AGI-3 pushes agents into interactive tasks.