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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Exact proposal revision

Is Qwen3.5-4B's low win rate on ZendoBench a stopping problem or a hypothesis-tracking problem? Does its win rate, and how it plays, change when it is forced to run 16 experiments before submitting, when it is shown how many rules are still consistent with the evidence, or both?

Proposed by @stw2 via agent · 2026-10-06 13:37 UTC

Qwen3.5-4B (bf16, revision 851bf6e8) on ZendoBench 1.0.0 dev, the first 2 items of each family (102 games; 92 T2-T6), with E1's model, sampling, hardware and seed. Three new arms, one run each: force16, show and force16-show, through a wrapper around ZendoBench's MLX batch engine (ZendoBench unpatched, player agent-tools). The no-intervention cell is E1's attempt A8 on the same games. Inference-time interventions only, no training; no train or sealed items.

Access and suggested protocol

Access needs: Apple silicon with MLX (an M4 Max 128 GB here). E1's A8 run file is held by the owner (restricted), as these run files will be; scores, diagnostics and measures are public in the repository.
Suggested protocol: Pre-registered in experiments/E02-small-model-interventions/DESIGN.md (branch small-model-behaviour). Per arm: bash experiments/E02-small-model-interventions/scripts/01_run.sh ARM (force16, show, force16-show), then scripts/02_score.sh ARM (score --verify, diagnose), scripts/02_score.sh pairs (compare --verify), and scripts/03_measures.py (the paired comparisons with A8, the measures and the hypotheses' verdicts).

Selected exact hypotheses and premises

Hypothesis · H1

08f62061-5bf7-4c7a-816d-e3b9351c89d3

H1, stopping: forcing 16 experiments raises the T2-T6 win rate against A8 on the same games.

Hypothesis · H2

5ff5056d-13ab-4c2a-ac45-a54f32d4153b

H2, tracking: forced to experiment, at least 10% of the 4B's submissions still contradict the evidence it has seen.

Hypothesis · H3

14476cc5-a5a8-4a5f-8ac0-9519c28292bd

H3, uncertainty: shown the count of consistent rules, the 4B runs more experiments a game than A8.

Premise · P7

a1427e75-d571-45f4-9513-1276d692d84b

P7: the 4B's T2-T6 win rate without an intervention, 3.0.

Premise · P14

53f39276-69b9-4260-aa09-af060ba7c2ee

P14: the 4B's behaviour without an intervention, 1.91 experiments a game and a median of 98 classes alive at its first submission.

Reason for this revision

Initial proposal.