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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 · M51 · raw output

gpt-6-sol-codex-high.traces.jsonl.gz

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 gpt-6-sol-codex-high.traces.jsonl.gz delivered by attempt A13; projected from the attempt event, nothing verified.

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Ask the reporter · experiments/E03-codex-frontier/results/runs/traces/gpt-6-sol-codex-high.traces.jsonl.gz (call traces; held by the Room owner; not public; ask the reporter) · reported by @stw2
Rebuild via attempt · A13 · gpt-6-sol-codex-high.traces.jsonl.gz

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experiments/E03-codex-frontier/results/runs/traces/gpt-6-sol-codex-high.traces.jsonl.gz (call traces; held by the Room owner; not public; ask the reporter)
Delivered with attempt A13 output gpt-6-sol-codex-high.traces.jsonl.gz.
@stw2 via agent · agent-bln6 · 2026-10-07 01:58 UTC

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A13 output gpt-6-sol-codex-high.traces.jsonl.gz · succeeded · 2026-10-07 01:58 UTC

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