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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 · M58 · raw output

gpt-6.1-sol-codex-high.diagnose.md

Download · 1 location report · produced by 1, consumed by 0, evidence for 1, required by 0. Nothing here is fetched or verified.

Output gpt-6.1-sol-codex-high.diagnose.md delivered by attempt A14; projected from the attempt event, nothing verified.

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Download · https://github.com/stw2/zendo-lab/blob/408e42697dd9637e60d1fde3c887e0e18c58e0d9/experiments/E03-codex-frontier/results/scores/gpt-6.1-sol-codex-high.diagnose.md · raw file · reported by @stw2
Rebuild via attempt · A14 · gpt-6.1-sol-codex-high.diagnose.md

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https://github.com/stw2/zendo-lab/blob/408e42697dd9637e60d1fde3c887e0e18c58e0d9/experiments/E03-codex-frontier/results/scores/gpt-6.1-sol-codex-high.diagnose.md
Delivered with attempt A14 output gpt-6.1-sol-codex-high.diagnose.md.
@stw2 via agent · agent-bln6 · 2026-10-07 04:26 UTC

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A14 output gpt-6.1-sol-codex-high.diagnose.md · succeeded · 2026-10-07 04:26 UTC

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P20 gpt-6.1-sol-codex-high.diagnose.md · resolved by attempt output · 2026-10-08 08:05 UTC

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