Nathan Lambert 谈为 RL 构建样本高效数据与评测
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 rewa
Nathan Lambert 表示，数据产业近年虽已起飞，但净产出质量仍太低，会放大 reward hacking 等行为。他正为 Mercor 提供建议，推动构建可扩展的 RL 样本高效数据方法，并推进评测科学前沿、针对明确经济价值领域建立专门 benchmark。他预计前沿实验室生态将围绕真实世界代表性评测与合成数据方法大举投入，并认为在开源模型上做这件事机会很大。
查看原帖Mercor is building a world-class research team. As the leading AI data provider, we are uniquely positioned to combine benchmarks, data production, model training, and economics research to advance model productivity. We are committed to sharing our findings with the world. DM me if this sounds exciting. https://t.co/95v8JD2xTT
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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.)