Tape: A Cellular Automata Benchmark for Evaluating Rule-Shift Generalization in Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914490593837056 |
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| author | Pan, Enze |
| author_facet | Pan, Enze |
| contents | Out-of-distribution generalization in reinforcement learning is hard to diagnose when benchmark shifts mix dynamics, observations, goals, and rewards. We address this with Tape, a controlled benchmark that isolates latent rule-shift in dynamics while keeping the observation-action interface fixed. The protocol combines deterministic splits, 20-seed replication, bootstrap uncertainty reporting, and continuous metrics for sparse-success regimes. Across baseline families, we find a consistent ID-to-OOD drop and strong heterogeneity across stable/periodic/chaotic rules. Importantly, this fragility appears even in an intentionally simple 1D deterministic setting, suggesting that many current RL algorithms remain brittle to latent-law changes under minimal confounds. To calibrate strict success, we report a protocol-matched true-dynamics random-shooting reference (p_oracle is almost 0.187) and oracle-normalized scores ON(p) = 100 p / p_oracle; this is a budgeted operational reference, not a global-optimality bound. A smaller feasibility regime (L = H = 16) with 100% rule-wise solvability helps separate reachability limits from policy failure. These results position Tape as a mechanism-oriented diagnostic for robust adaptation and latent-mechanism inference, and as a controlled benchmark relevant to broader AGI-oriented evaluation without making strong AGI sufficiency claims. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04695 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tape: A Cellular Automata Benchmark for Evaluating Rule-Shift Generalization in Reinforcement Learning Pan, Enze Artificial Intelligence Machine Learning 68T01 Out-of-distribution generalization in reinforcement learning is hard to diagnose when benchmark shifts mix dynamics, observations, goals, and rewards. We address this with Tape, a controlled benchmark that isolates latent rule-shift in dynamics while keeping the observation-action interface fixed. The protocol combines deterministic splits, 20-seed replication, bootstrap uncertainty reporting, and continuous metrics for sparse-success regimes. Across baseline families, we find a consistent ID-to-OOD drop and strong heterogeneity across stable/periodic/chaotic rules. Importantly, this fragility appears even in an intentionally simple 1D deterministic setting, suggesting that many current RL algorithms remain brittle to latent-law changes under minimal confounds. To calibrate strict success, we report a protocol-matched true-dynamics random-shooting reference (p_oracle is almost 0.187) and oracle-normalized scores ON(p) = 100 p / p_oracle; this is a budgeted operational reference, not a global-optimality bound. A smaller feasibility regime (L = H = 16) with 100% rule-wise solvability helps separate reachability limits from policy failure. These results position Tape as a mechanism-oriented diagnostic for robust adaptation and latent-mechanism inference, and as a controlled benchmark relevant to broader AGI-oriented evaluation without making strong AGI sufficiency claims. |
| title | Tape: A Cellular Automata Benchmark for Evaluating Rule-Shift Generalization in Reinforcement Learning |
| topic | Artificial Intelligence Machine Learning 68T01 |
| url | https://arxiv.org/abs/2601.04695 |