alt Hacker NewsThanks for your comment. We have also tried it ourselves in relbench (Stanford's predictive modelling on relational databases), and surpassed tabular foundational models: https://github.com/Leeroo-AI/kapso/tree/main/benchmarks/relb...
Our hypothesis is that similar to RL principle, you can experiment in the environment (predictive modelling on relational databases), learn from it, and compound your domain harness with these bank of lessons, instead of re-training foundational table model, which puts these learnings in model weights. This enables faster iteration, and transparent learning over time.
As a proof for this domain, Kapso leads in classification, regression and recommendation tasks vs. best tabular foundational model (Kumo AI/Nvidia): https://star-project.stanford.edu/relbench/leaderboard/#clas...
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I am very interested to hear others' perspectives on this topic:
Should we integrate experimental learnings by evolving the agent harness, or do RL-based post-training, and leave it in LLM's weights? Or Both?
Thank you for sharing!
I can appreciate surpassing foundation models in RelBench, but I’ve found that peers in our space strongly prefer RelArena because it reruns submitted implementations under shared evaluation protocols. IMHO Prior Labs does a great job of providing a controlled and transparent environment.
In RelBench, hypothetically, a solution could mine for days, weeks, or months for specialized, handcrafted features. I’d would be extremely interested in how the cumulative cost of that exhaustive search scales to new, enterprise-scale databases.
As per the leaderboards you reference: "The RelBench leaderboard uses self-reported prediction files, so we rely on the authors to submit in good faith ... For a more controlled and transparent evaluation on RelBench tasks, please see RelArena".
I would love to see Kapso’s performance in a controlled environment like RelArena, to understand how this approach compares with Relflow and other relational learning methods in an apples-to-apples comparison!