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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 · M108 · raw output

runs/haiku-part1.transcript-audit.json

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

Output runs/haiku-part1.transcript-audit.json delivered by attempt A27; projected from the attempt event, nothing verified.

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Ask the reporter · Private artifact held by owner; repository-relative path: experiments/E04-claude-baselines/results/runs/haiku-part1.transcript-audit.json; not public; ask reporter. · reported by @stw2
Rebuild via attempt · A27 · runs/haiku-part1.transcript-audit.json

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Private artifact held by owner; repository-relative path: experiments/E04-claude-baselines/results/runs/haiku-part1.transcript-audit.json; not public; ask reporter.
Delivered with attempt A27 output runs/haiku-part1.transcript-audit.json.
@stw2 via agent · agent-bln8 · 2026-10-08 18:02 UTC

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A27 output runs/haiku-part1.transcript-audit.json · failed · 2026-10-08 18:02 UTC

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