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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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Finding · P14 · Author-curated

On ZendoBench 1.0.0 dev (460 games), Qwen3.5-4B (MLX) runs 1.91 experiments per game at 0.78 bits of expected information each, and first submits with a median of 98 rule classes still consistent with the evidence (mean win probability 0.09; 460 games with a submission). E1 attempt A8.

Published by @stw2 · 2026-10-06 · Sources, measurements and interpretation are supplied by the author.

Structured assertion

Relation: Behaviour profile

subject: Qwen3.5-4BThe model evaluated.
setting: E1 setting qwen3.5-4b-mlxBackend, hardware, sampling and harness settings of the run.
evaluation items: ZendoBench 1.0.0 devThe games played.
games: 460 gamesScored games described (all tiers, T1 included).
experiments per game: 1.91 experiments per gameValue of the concept experiments_per_game (defined in the seed-only finding).
expected information gain: 0.775 bits per experimentValue of the concept expected_information_gain.
classes alive at first submission: 98 rule classesValue of the concept classes_alive_at_first_submission (a median).
win probability at first submission: 0.090 probabilityValue of the concept win_probability_at_first_submission (a mean).
games with a submission: 460 gamesGames over which the two first-submission statistics are taken.
preregistered: trueWhether DESIGN.md (E1) named these measures before measuring (as descriptive measures).

Experimental provenance

Method and evaluation protocol: E1: `zendo_bench diagnose` (ZendoBench 1.0.0) over the attempt's run files, from each game's engine record alone.
Dataset: ZendoBench 1.0.0 dev manifest: 460 games (T1 30, T2 60, T3 150, T4 60, T5 100, T6 60), Room material M1Version: zendo-bench v1.0.0 (git 46c192e0ea10a5140a33c1280edb97b0127cc68c); manifest sha256 e99344a3c3c9e624bf1c0f5782b266b54e97187cf412d9ff2dbe09d0c1e0a5c2 · Access: public
Reported results: exp_per_game 1.9130, bits_per_exp 0.7654, eig 0.7752, zero-information share 0.0443, alive_first 98.0, p_first 0.0901, submissions per game 1.9891, contradicting submissions 177/915, counterexample uptake 402/455.
Uncertainty and replication: Descriptive; no interval. Medians and means over the stated games.
Evidence references: qwen3.5-4b-mlx.jsonl · restrictedexperiments/E01-dev-baseline/results/runs/qwen3.5-4b-mlx.jsonl (held by the Room owner; not public; ask the reporter)qwen3.5-4b-mlx.diagnose.md · publichttps://github.com/stw2/zendo-lab/blob/4480176a5cea03129154cf4e2f4621dafd13da3b/experiments/E01-dev-baseline/results/scores/qwen3.5-4b-mlx.diagnose.md
Limitations: One run per system on the dev split, not sealed. Each system runs at its publisher's recommended sampling and reasoning effort, so settings differ between systems. Dev rule catalogs are public; ZendoBench makes no contamination-free claim. Answers are grammar-constrained, so its malformed rate (0) is not comparable with the HTTP systems'. Rerun of A7, which failed its preflight on a corrupt local checkpoint cache. Covers all 460 games including T1, while the headline covers T2-T6.

Concept definitions

Reuse the defining version and key when the meaning fits your assertion.

Behaviour profile

Predicate. Descriptive statistics of how the subject plays the evaluation items under the setting, from zendo_bench diagnose (ZendoBench 1.0.0): how much it experiments, the information its experiments gain, and how much uncertainty remains at its first submission. No interval.

Key behaviour_profile · version 7f391bde-3e66-43b6-9a50-d299f7b7e5b2

Concept JSON · Defining publication

ZendoBench 1.0.0 dev

Evaluation items. The 460 games of ZendoBench 1.0.0's dev manifest (T1 30, T2 60, T3 150, T4 60, T5 100, T6 60), file sha256 e99344a3c3c9e624bf1c0f5782b266b54e97187cf412d9ff2dbe09d0c1e0a5c2 (Room material M1), from github.com/stw2/zendo-bench tag v1.0.0, commit 46c192e. In a game a hidden rule labels scenes of 1 to N pieces; the player builds scenes to have them labelled (experiments, budget 30) and submits rules (at most 2). Close to exact learning from membership and equivalence queries.

Key zendobench_1_0_0_dev · version 7f391bde-3e66-43b6-9a50-d299f7b7e5b2

Concept JSON · Defining publication

E1 setting qwen3.5-4b-mlx

ZendoBench 1.0.0 MLX batch driver on Apple M4 Max 128 GB (mlx 0.32.2), bf16, 36 games in flight, sampler seed 0; temperature 1.0, top_p 0.95, top_k 20, min_p 0, presence penalty 1.5; thinking budget 63,487 tokens then a 2,048-token answer allowance (a call's cap 65,536), answers constrained to valid actions; reasoning kept; player model; `bash experiments/E01-dev-baseline/scripts/01_run.sh qwen3.5-4b-mlx`.

Key arm_qwen3_5_4b_mlx · version a1427e75-d571-45f4-9513-1276d692d84b

Concept JSON · Defining publication

Qwen3.5-4B

Qwen3.5-4B open weights (bf16), Hugging Face Qwen/Qwen3.5-4B at revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a, ZendoBench 1.0.0's pinned 4B checkpoint (shard SHA-256 checked on load).

Key qwen3_5_4b · version a1427e75-d571-45f4-9513-1276d692d84b

Concept JSON · Defining publication

Exact references

a1427e75-d571-45f4-9513-1276d692d84b

The headline estimate from the same attempt.

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