New Research Challenges the Scale-Is-All-You-Need Orthodoxy
An MIT paper on recursive self-revision and OpenAI's work on internal LLM circuits suggest that architecture and process innovations may matter as much as raw scale for the next generation of models.
A cluster of research papers shared this week points to a subtle but important shift in how the field thinks about model improvement. @rryssf_ highlighted an MIT paper on Recursive Language Models, which demonstrates that allowing models to iteratively revise their own outputs outperforms simply scaling up parameters. The finding is notable because it suggests a compute-efficient alternative to the brute-force scaling paradigm that has dominated since GPT-3.
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