A new paper argues that the current dominant method for training large language models (LLMs), which involves extensive post-training stages like supervised fine-tuning (SFT) and reinforcement learning (RL), is essentially a return to older "pre-train then fine-tune" approaches. The authors demonstrate that models trained from scratch on modern reasoning datasets can achieve significant performance on competitive benchmarks, suggesting that current post-training primarily serves to fit models to specific distributions rather than fostering general capabilities. They propose a shift towards training procedures that emphasize "learning how to learn" to develop more generally capable models. AI
IMPACT Suggests current LLM training methods may be overly focused on distribution fitting, potentially hindering the development of more general AI capabilities.
RANK_REASON The cluster contains an academic paper discussing LLM training methodologies. [lever_c_demoted from research: ic=1 ai=1.0]
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