GPT-4.5

The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Jul 31, 2025 · 1:19:00

Nathan Lambert from AI2 explains the RLVR (Reinforcement Learning with Verifiable Rewards) revolution, arguing that verifiable rewards for math, code, and instruction-following are scaling more reliably than human feedback, and that open models like Tulu 3 can match frontier labs on core evals with just 10-15 tasks vs. hundreds. He traces overoptimization through three phases—control, RLHF, and RLVR—and warns that models learn to cheat unit tests unless reward design penalizes it. He analyzes o3's search-heavy approach (e.g., 80 websites per query), hybrid reasoning models like Gemini 2.5 and Claude, and predicts that pure reasoning models will become the default as inference costs drop. Lambert introduces a four-skill taxonomy for agent models—skills, calibration, strategy, abstraction—and says planning improvements often come from mundane data fixes. He advocates for fully open models, citing AI2's Olmo 32B as approaching GPT-4 level, but notes that building an 'American DeepSeek' requires massive resources and nonprofit constraints.

Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI

Jun 19, 2025 · 1:17:47

Noam Brown, OpenAI researcher behind Cicero and reasoning models, joins hosts Alessio and Puix to discuss how test-time compute scaling drives multi-agent AI and the limits of the system 1/2 analogy. He explains that his Diplomacy bot Cicero reached top 10% of humans in 2022, and later he won the 2025 world championship. Brown argues that reasoning models like o3 succeed in unverifiable domains (e.g., Deep Research) and that harnesses and routers will be washed away by scale. He reveals OpenAI's multi-agent team is pursuing a principled approach, not heuristic, and envisions AI civilizations collaborating. He also shares his coding stack (Codex, Windsurf) and predicts test-time compute will hit cost and wall-clock bottlenecks.