Test-Time Scaling & Agent Adaptation
Key Questions
What is test-time scaling?
Test-time scaling claims that overtraining combined with test-time compute outperforms traditional pretraining, as highlighted in recent arXiv papers.
What is Cog-DRIFT?
Cog-DRIFT enables reinforcement learning from zero-reward examples (RLVR) on hard tasks through reformulation, improving model performance on challenging problems.
How does self-execution improve coding LLMs?
Self-execution simulation verifies and enhances coding LLMs by simulating execution to check reasoning and outputs.
What do Stanford findings say about agents?
Stanford research shows single agents outperform multi-agents in efficiency.
What are agent adaptation policies?
Agent adaptation policies improve efficiency without retraining, amid challenges to traditional scaling norms in the agent landscape.
arXiv claims overtraining + test-time compute beats pretraining; agent adaptation policies for efficiency w/o retraining. Cog-DRIFT enables RLVR on zero-reward hard tasks via reformulation (ex-3ff060f1, ex-4ded5acb); self-execution verifies coding LLMs (ex-30da5d65); Stanford single agents outperform multi-agents on efficiency (ex-586625cc). Challenges scaling norms amid agent buzz.