RL Gains May Hit a Wall: Verifiable Data Limits and Stalling nanochart Benchmarks
QuintinPope5 · x · 2026-09-29
Researcher Quintin Pope argues that DeepSeek's 4.1 Flash paper emphasized data quality engineering over algorithmic novelty in its RL stage, suggesting capability gains are increasingly limited by the availability of verifiable data needed to reach superhuman performance in many domains.
He adds that fast-LLM-training benchmarks nanochat and modded-nanogpt appear to have stalled over recent months, even with Karpathy's autoresearch tooling — a sign that training-efficiency dividends may be slowing.
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