Public room

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.

Started by @stw2
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← Current experiment E4

Exact proposal revision

Where do Claude Haiku, Sonnet, Opus and Fable stand on ZendoBench 1.0.0 dev at high reasoning effort through a tool-free Claude Code harness matched as closely as possible to E3, and how do they experiment and decide to submit?

Proposed by @stw2 via agent · 2026-10-08 09:14 UTC

Descriptive inference-only baselines: claude-haiku-5-5, claude-sonnet-5-5, claude-opus-5-5, claude-fable-5-1; high effort; all 460 dev games per model, episode seeds from the manifest and no model seed; provider-hosted inference orchestrated on Apple silicon. Arms run sequentially Haiku, Sonnet, Opus, Fable, with 12 calls in flight within an arm. No training or sealed evaluation.

Access and suggested protocol

Access needs: Claude subscription access to all four specified models and macOS for the isolation sandbox. Login and raw run files stay private. Additional account access or unresolved isolation differences stop preflight; no silent model substitution.
Suggested protocol: Match E3: unpatched ZendoBench 1.0.0 runner, player model, ungrammared replies, one fresh CLI invocation per decision and unchanged benchmark prompts. Pin Claude Code and model IDs, remove tools and customizations, isolate configuration and filesystem access. Preflight must verify actual prompts, tools, model, high effort, output allowance and complete transcript capture before evaluation. Claude requires a finite output allowance; target the largest supported limit (documented 128K), record the exact effective value and provider sampling differences. Save all exposed reasoning, streamed events, prompts, retries and failures locally with trace-to-game joins; publish only sanitized summaries and digests. Score every run with --verify and compare the same dev games against E3. Register attempts before launch; commit DESIGN.md before measurements.

Selected exact hypotheses and premises

No hypotheses or premises selected.

Reason for this revision

Initial proposal.