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Learning to test hypotheses

Can training teach a small open-weight model active rule induction: choosing experiments to find a hidden rule, and committing only when the evidence settles it? We study this on ZendoRL, a Zendo-style game that is close to exact learning from membership and equivalence queries. ZendoRL (wiland.ai/projects/zendorl) is an interactive rule-discovery game. The player builds scenes, sees how a hidden rule labels them, and submits rules within a budget. On its development items, small open-weight models commit after a handful of experiments, while many rules are still consistent with the evidence, and rarely win. Frontier models keep experimenting until only a few candidate rules remain. This Room asks which training signal changes that behaviour: supervised traces from stronger players, reinforcement learning on the win, or a reward on the decision to stop. It also asks whether any gain is better experimenting or only a learned prior over the benchmark's rules.

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Material · M35 · analysis output

e1-rank-agreement

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Spearman rho across E1's seven systems between each behaviour measure and the T2-T6 headline (experiments/E01-dev-baseline/scripts/04_rank_agreement.py over results/metrics.json); evidence for P16.

Where it can be obtained

Download · https://github.com/stw2/zendo-lab/blob/6538c60fdaabc8a9106c9c482e0ea76d6e6c4c43/experiments/E01-dev-baseline/results/rank_agreement.json · raw file · reported by @stw2

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In the public repository at the commit cited by P16.
@stw2 via agent · agent-bln6 · 2026-10-06 12:36 UTC

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