RLMM runtime stays within approximately 15–20 seconds across the four peg-solitaire boards while MDP-MM runtime grows with task complexity, reaching a 19.1× speedup on the diamond board¶
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Evidence · 1 study · designr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
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This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? RLMM estimation runtime remains stable as state-space size grows, whereas MDP-MM runtime increases by an order of magnitude from the tiny cross to the diamond board. → Wenqian Xu 2026
Evidence¶
Wenqian Xu 2026¶
Wenqian Xu, Feng Ji. (2026). Reinforcement Learning Measurement Model. arXiv. https://arxiv.org/abs/2605.09305 doi:10.48550/arxiv.2605.09305
q2 · i? · design · r2
Runtime comparison of the second estimation stage on the four peg-solitaire boards (Table 4): MDP-MM 35.30 s to 370.53 s versus RLMM 16.45 s to 19.35 s, with printed speedups up to 19.1×. The article attributes stability to avoiding "repeatedlysolvingthefullMDPduringparameterupdates".
"Incontrast,theruntimeoftheRLMMremainsstableacrossallfourenvironments,stayingwithinanarrow rangeofapproximately15–20seconds."
Discussion¶
Related Claims¶
- Influence-function diagnostics identify steps 3–8 of the Big-L peg-solitaire board as the most consequential region, with the lowest-β participants showing substantially larger absolute influence values there — related
- In peg-solitaire simulations with known true abilities, the RLMM attains lower RMSE of log βj than the MDP-MM on all four original boards — related
- The RLMM recovers ability parameters on peg-solitaire boards with up to more than 23 million states, where tabular MDP-MM estimation is impractical — related
- In AQUALAB, estimated β is ordered across episode completion status (means 1.181, 1.506, 2.343; Kruskal–Wallis p = 8.62×10−29) and higher-β quintiles complete more content more efficiently
- In AQUALAB gameplay logs, the estimated RLMM person parameter is positively associated with cumulative trajectory reward (Pearson 0.5842, Spearman 0.5760)
- Hint Factory MDP-generated hints gave correct next-step hints over 80% of the time and a pilot showed students solved more logic proofs with hints — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
rlmm-stable-runtime-speedup-growing-complexity |
title |
RLMM runtime stays within approximately 15–20 seconds across the four peg-solitaire boards while MDP-MM runtime grows with task complexity, reaching a 19.1× speedup on the diamond board |
description |
RLMM runtime stays within approximately 15–20 seconds across the four peg-solitaire boards while MDP-MM runtime grows with task complexity, reaching a 19.1× speedup on the diamond board |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — wenqian-xu-2026 |
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