RuiWang0811

5 ms·

1.
by RuiWang0811 1mo ago
there are loads of env businesses for coding tasks, enterprise tasks, computer use etc etc. we offer different envs from a niche industry, which just so happens to be a very hard data science task & where the data doesn't saturate.
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by RuiWang0811 1mo ago
We have experiments showing that agents at least can learn from the environment by overfitting on train. But we do not yet have full post train runs, mainly due to time. But follow our blog/X where we’ll regularly update our research
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by RuiWang0811 1mo ago
We use real historical market data for the environments. There is no parametric modelling involved. The decay property refers to alpha that we give the agent for trade in the env - they are generated as tools.
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by RuiWang0811 1mo ago
No we sell our own research to AI labs as RL envs. Realistic RL envs grows in demand as labs seek better data train better models. It’s a complimentary business. Simply put: we sell envs to labs, labs make better models, firms buy these mod
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by RuiWang0811 1mo ago
languagelearner, I think you need to spend more time learning languages
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by RuiWang0811 1mo ago
this seems to be a common misconception, our envs use market data, but the goal is not (only) trading. Market data just happens to be a good source of hard data science tasks. Re trading: I’d argue there is no such thing as solving investme
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by RuiWang0811 1mo ago
we do affine transformations of the data, so all return/ pnl measures are still the same as with untransformed data. The transformation doesn’t change the conditional distribution of the data, which is what alphas ultimately measure
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by RuiWang0811 1mo ago
not sure about your background, the trace shows the feature engineering the LLMs did
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by RuiWang0811 1mo ago
cofounder here - LLMs can do some model training, they train on ML competition data after all. But they do struggle with low signal to noise ratio of market data. But that’s exactly what our environments will teach.
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by RuiWang0811 2mo ago
Does this mean you have to retrain routing rules every time a new model gets released? I imagine since the price/token (or rather the amount of work that can be done per token) does not monotonically increase with new models, that the
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by RuiWang0811 2mo ago
With all the benchmaxxing happening, current evals are oversaturating and become meaningless for model comparison. I think there is only one test that can't be gamed: let them trade in real markets. Markets are self-improving, as model