The State of Reasoning — from Nathan Lambert, Interconnects/AI2 [LS Live @ NeurIPS 2024]
Jan 2, 2025 · 16:22
Nathan Lambert argues that language models do perform reasoning, contrary to skeptics, and that embracing chain-of-thought and reinforcement learning (RL) is key to advancing their capabilities. He explains OpenAI's o1 as large-scale RL on verifiable outcomes, noting that post-training flops exceed pre-training, and highlights relatives like DeepSeek and Qwen which are narrower. Lambert details OpenAI's new reinforcement fine-tuning API, which uses the same infrastructure as o1 and requires only dozens of labeled samples, and contrasts it with process reward models or Monte Carlo tree search. He presents his own project using RL on math evaluations (GSM8K, MATH, AFeval) to show gentle RL fine-tuning can boost specific capabilities without degrading general performance. The talk concludes that reasoning is worth pursuing and that new, less-human-like forms of model reasoning are emerging.
