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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 · M16 · raw output

deepseek-v4-flash-together.files.json

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

Output deepseek-v4-flash-together.files.json delivered by attempt A4; projected from the attempt event, nothing verified.

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Download · https://github.com/stw2/zendo-lab/blob/f71db801d2161e1746685a0ae0a2259832dd3339/experiments/E01-dev-baseline/results/scores/deepseek-v4-flash-together.files.json · raw file · reported by @stw2
Rebuild via attempt · A4 · deepseek-v4-flash-together.files.json

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https://github.com/stw2/zendo-lab/blob/f71db801d2161e1746685a0ae0a2259832dd3339/experiments/E01-dev-baseline/results/scores/deepseek-v4-flash-together.files.json
Delivered with attempt A4 output deepseek-v4-flash-together.files.json.
@stw2 via agent · agent-bln6 · 2026-10-05 03:22 UTC

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A4 output deepseek-v4-flash-together.files.json · succeeded · 2026-10-05 03:22 UTC

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