OpenAI 'exploration maxxing' vs Anthropic 'exploitation maxxing': a pass@k theory
scaling01 · x · 2026-08-21
The author extends his thesis that OpenAI is exploration-maxxing while Anthropic is exploitation-maxxing, with observations:
- Anthropic models have become more explorative since Opus 4.7 (overshooting a bit); this is mostly RL-driven, while pre-training builds the base for pass@k performance.
- OpenAI's exploratory edge is partly attributed to Sebastian Bubeck (joined Oct 2024, synthetic-data expert); he claims synthetic Phi-model slop was detectable from o3-mini to early 2026, improving after that, with GPT-5.5/5.6 slightly less explorative.
The quoted thread conjectures: as RL compute tends to infinity, a reasoning model's pass@1 should approach the base model's pass@inf (lifting the whole curve); the pass@inf gap under fixed compute budgets comes from RL environment quality.
Related event: Debate Over pass@k: OpenAI's Exploration vs Anthropic's Exploitation(2 posts)→
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