Research acceleration: Cog-DRIFT RLVR + Stanford multi-agent myth + SkillX/FileGram + Self-Distilled RLVR + Vero/AlphaEvolve + Meta Harness + Agentic-MME + LeCun/OpenWorldLib + Karpathy + ClawArena + RL scaling/benches
Key Questions
What is Cog-DRIFT?
Cog-DRIFT breaks RLVR exploration stalls by learning from zero-reward examples. It enables progress on hard problems.
What did Stanford research find on multi-agents?
Stanford's paper shows multi-agents yield no gains matching compute increases. More agents do not always improve results.
What is SkillX and FileGram?
SkillX auto-generates skills; FileGram personalizes file systems. They advance agent capabilities.
What is Self-Distilled RLVR?
Self-Distilled RLVR improves models via self-execution simulation. It enhances coding and reasoning.
What is Vero?
Vero is an open RL recipe for general visual reasoning. It includes AlphaEvolve elements.
What is Meta-Harness?
Meta-Harness optimizes model harnesses end-to-end. It supports agentic research acceleration.
What is Agentic-MME?
Agentic-MME evaluates agentic capabilities in multimodal intelligence. It explores learn-to-learn and self-execution.
What benchmarks are advancing RL scaling?
ClawArena, ARC-3, and H-Bench drive RL scaling research. Karpathy notes 11% improvements.
Cog-DRIFT breaks RLVR exploration stall; Stanford: multi-agents no gain matched compute; SkillX auto-skills/FileGram FS personalization; Self-Distilled RLVR/Vero visual/DeepMind MARL/Meta Harness; Agentic-MME/learn-to-learn/self-execution; LeCun JEPA/OpenWorldLib; Karpathy 11%; ClawArena/ARC-3/H-Bench; RL scaling.