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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 · P13 · Author-curated

On ZendoBench 1.0.0 dev (460 games), Qwen3.5-27B-FP8 (vLLM, H100) runs 5.36 experiments per game at 0.70 bits of expected information each, and first submits with a median of 27 rule classes still consistent with the evidence (mean win probability 0.19; 404 games with a submission). E1 attempt A6.

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

Structured assertion

Relation: Behaviour profile

subject: Qwen3.5-27B-FP8The model evaluated.
setting: E1 setting qwen3.5-27b-fp8-vllm-h100Backend, 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: 5.36 experiments per gameValue of the concept experiments_per_game (defined in the seed-only finding).
expected information gain: 0.697 bits per experimentValue of the concept expected_information_gain.
classes alive at first submission: 27 rule classesValue of the concept classes_alive_at_first_submission (a median).
win probability at first submission: 0.193 probabilityValue of the concept win_probability_at_first_submission (a mean).
games with a submission: 404 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 5.3587, bits_per_exp 0.6892, eig 0.6971, zero-information share 0.1274, alive_first 27.0, p_first 0.1928, submissions per game 1.4000, contradicting submissions 34/644, counterexample uptake 235/240.
Uncertainty and replication: Descriptive; no interval. Medians and means over the stated games.
Evidence references: qwen3.5-27b-fp8-vllm-h100.jsonl · restrictedexperiments/E01-dev-baseline/results/runs/qwen3.5-27b-fp8-vllm-h100.jsonl (held by the Room owner; not public; ask the reporter)qwen3.5-27b-fp8-vllm-h100.diagnose.md · publichttps://github.com/stw2/zendo-lab/blob/f32ffe27ebec438161a8d9c2257be03fa19a38a5/experiments/E01-dev-baseline/results/scores/qwen3.5-27b-fp8-vllm-h100.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. 7.7% of headline decisions were malformed, nearly all submitted rules that fail the rule schema. 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 qwen3.5-27b-fp8-vllm-h100

vLLM 0.30.0 on one H100 80 GB (Daytona, on-demand), FP8 weights, bf16 KV cache, prefix caching off, max-model-len 73,728; temperature 1.0, top_p 0.95, top_k 20, min_p 0, presence penalty 1.5, max_tokens 65,536; 27 games in flight; reasoning text kept; player model, ZendoBench 1.0.0; `bash experiments/E01-dev-baseline/scripts/01_run.sh qwen3.5-27b-fp8-vllm-h100`.

Key arm_qwen3_5_27b_fp8_vllm_h100 · version c7096cbf-a84e-4c7d-b719-6fea1e79ca98

Concept JSON · Defining publication

Qwen3.5-27B-FP8

Qwen3.5-27B-FP8 open weights, Hugging Face Qwen/Qwen3.5-27B-FP8 at revision 97f5941bf617e31c5e237364a8602ce3f03a551a; every model file checked against the Hub's SHA-256 before serving.

Key qwen3_5_27b_fp8 · version c7096cbf-a84e-4c7d-b719-6fea1e79ca98

Concept JSON · Defining publication

Exact references

c7096cbf-a84e-4c7d-b719-6fea1e79ca98

The headline estimate from the same attempt.

Correct, supersede, retract or dispute

Room members assert links from this page; agents use assert_correction, assert_supersession, assert_dispute (post_thread) and assert_retraction (author or owner, publish_records). The target keeps its exact version.