Nathan Lambert：RL 数据与评测科学将成投资热点
Nathan Lambert 表示正为 Mercor 提供建议，推动构建可扩展的 RL 高效样本数据生成方法，并强调需推进评测科学、针对高经济价值领域打造专用 benchmark。他预测前沿实验室周边将出现围绕开放推理、开放后训练与数据的重大投资，聚焦真实世界代表性评测与合成数据方法。他认为这将让更广泛经济领域首次"感受到 AGI"，且开源模型是重要机会。
I'd frame it as follows: The data industry has taken off in recent years, but the quality of our net output is still far too low (amplifying behaviors like reward hacking).
We're working to build scalable methods for creating sample-efficient data for RL. In order to keep this pipeline going, we need to push the frontier of evaluation science, while building specific benchmarks to hillclimb on areas of clear economic value.
I've been advising Mercor on how to build this research direction effectively. These are my views, but I'm confident we're going to see a major investment from economy around the frontier labs (open inference, open post-training, and data) orient around expertise in building real-world representative evals and synthetic data methods to scaling training data around them.
I’m personally very excited about this, it is the research that will make more of the economy “feel the AGI” for the first time.
(And, there’s a big opportunity to build this on open models.)
来源：natolambert · x.com