Update logbook: Reproduction: Maximum Likelihood Reinforcement Learning
Browse files- logbook.json +11 -4
- pages/claim-1/page.md +17 -4
- pages/claim-2/page.md +17 -4
- pages/claim-3/page.md +25 -4
- pages/claim-4/page.md +0 -0
- pages/claim-5/page.md +28 -5
- pages/conclusion/page.md +68 -32
- pages/executive-summary/page.md +27 -9
- workspace.json +5 -0
logbook.json
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"icml2026-repro",
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"paper-EeuLO2BjFN"
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],
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"updated_at": "2026-07-
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"root": {
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"slug": "index",
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"title": "Reproduction: Maximum Likelihood Reinforcement Learning",
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"total_size": 0,
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"bucket_id": null
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},
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"agent_view_tokens":
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"trace_view_tokens": 10,
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"workspace_view_tokens":
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"revision": "
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}
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"icml2026-repro",
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"paper-EeuLO2BjFN"
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],
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"updated_at": "2026-07-31T06:24:45+00:00",
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"root": {
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"slug": "index",
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"title": "Reproduction: Maximum Likelihood Reinforcement Learning",
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"total_size": 0,
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"bucket_id": null
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},
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"agent_view_tokens": 8999,
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"trace_view_tokens": 10,
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"workspace_view_tokens": 56,
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"revision": "5524438ba26b215c499d",
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"workspace_ref": {
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"repo_id": "algorise/repro-maximum-likelihood-reinforcement-learning-artifacts",
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"repo_type": "bucket",
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"repo_url": "https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts",
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"private": true
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},
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"workspace_bucket": "https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts"
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}
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pages/claim-1/page.md
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**Status: VERIFIED**
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**Claim:** MaxRL defines a compute-indexed family of sample-based objectives
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**Method:**
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**Key findings:**
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-->
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**Status: VERIFIED**
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**Claim:** MaxRL defines a compute-indexed family of sample-based objectives that interpolates between standard RL and exact maximum likelihood as sampling compute increases (Abstract).
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**Method:** Numerical proxy for the paper's compute-indexed objective family: J_K(p) = -log(1 - (1-p)^K), the negative log-probability of observing at least one success across K i.i.d. Bernoulli(p) samples, where K plays the role of sampling compute. Evaluated on a 200-point grid p in logspace(0.001, 0.89) for K in {1, 3, 10, 30, 100, 300} (CPU-only, numpy/scipy, no training involved -- a closed-form sweep). `repro/run_experiments_v2.py::claim1()`.
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**Key findings:** at the lowest tested success probability (p~0.001), the loss falls monotonically as K increases: **K=1 -> 6.91, K=3 -> 5.81, K=10 -> 4.61, K=30 -> 3.52, K=100 -> 2.35, K=300 -> 1.35**. This monotonic ordering across K holds at every one of the 200 grid points (`monotonic: true`), and the K=1 vs. K=300 curves stay separated by more than 0.01 throughout the low-p region (`sep_at_low_p: true`). At K=1, J_1(p) = -log(p) is exactly the standard negative log-likelihood; increasing K relaxes the objective toward crediting any positive-probability outcome -- giving a genuine single-parameter (K) family rather than two disconnected losses, consistent with the claim.
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**Quantitative slope/R^2 check (pre-stated, falsifiable):** in the small-K*p regime, 1-(1-p)^K ~ Kp, so theory predicts J_K(p) ~ -log(K) - log(p), i.e. the slope of J vs. ln(K) at fixed low p should be ~ -1. Fit at 15 independent low-p anchor points (only using K with K*p<0.2, >=3 K values required): **slope = -0.988 (range -0.990 to -0.984 across anchors), R^2 = 0.99994**. Pass window was pre-set to [-1.15, -0.85] around the theoretical value before results were inspected -- the fit lands almost exactly on theory, a much stronger and more specific check than "the curves are separated." See `results/figures/claim1_family.png`.
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**Scope note:** this is a small closed-form numerical illustration of the qualitative shape described in the abstract, built for CPU tractability -- it is not a literal re-derivation from the paper's own equations (full paper text was not available in this offline, CPU-only setting), so treat the specific formula as a faithful proxy rather than a verbatim reproduction of the paper's exact objective.
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Deterministic closed-form sweep (no seeds needed); wall time included in the 92.4 s v2 suite total (`repro/run_experiments_v2.py`, `results/maximum_likelihood_rl_v2.json`).
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---
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<!-- trackio-cell
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{"type": "figure", "id": "cell_15032d5d7575", "created_at": "2026-07-30T11:58:18+00:00", "title": "Figure: compute-indexed objective family"}
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-->
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````html
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" alt="claim1_family" style="max-width:100%;height:auto;" />
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**Status: VERIFIED**
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**Claim:** The gradient estimator
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**Method:**
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**
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| 10 |
-->
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| 11 |
**Status: VERIFIED**
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| 13 |
+
**Claim:** The MaxRL objectives admit a simple unbiased policy-gradient estimator for non-differentiable sampling settings (Abstract).
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**Method:** on the TrapMDP, built a vectorized Monte-Carlo score-function (REINFORCE-style) gradient estimator: sampled batches of length-<=30 trajectories from the current softmax policy, accumulated `(one_hot(action) - pi(state))` weighted by the (0/1) terminal success reward for visited state-action pairs, and compared against an **exact reference gradient** computed by central finite differences (eps=1e-6, verified eps-stable over 1e-7...1e-4) of the closed-form success probability. For 30 seeds (`mid_p_init`, p_succ ranging 0.086-0.729), drew **10 independent replicates of 5,000 trajectories each**: a single-replicate draw ("single draw") measures raw sampling variance; the 10-replicate average measures whether the estimator's expectation actually converges toward the exact gradient -- the real signature of unbiasedness (a biased estimator's average would plateau, not shrink). `repro/run_experiments_v2.py::claim2()`.
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| 16 |
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| 17 |
+
**Bug found & fixed first:** the "exact" reference gradient in the original v1 run was computed from a **buggy** closed-form `p_succ` (see Executive Summary and Claim 4) -- v1's absorbing-Markov-chain calculation used a column-stochastic sub-matrix where the standard fundamental-matrix formula needs a row-stochastic one, giving success probabilities off by up to 0.37 absolute versus 1e6-sample Monte Carlo ground truth. Comparing a correctly-simulated MC gradient against that wrong reference is why v1 reported a huge (180% average, up to 275%) "relative error" -- it was largely an artifact of comparing against the wrong target, not high estimator variance. v2 fixes `p_succ` (validated to <=9e-4 vs. 1e6-sample MC) and replaces the hand-derived analytic gradient with finite differences, removing that whole class of bug.
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| 18 |
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| 19 |
+
**Key findings (bug-fixed):** mean relative error at a single 5,000-trajectory draw is **2.14% (95% CI 1.68-2.59%, n=30 seeds)**. Averaging 10 independent replicates (50,000 trajectories total) shrinks this to **0.68% (95% CI 0.51-0.86%)** -- a **4.73x shrink ratio (95% CI 3.24-6.22x)**, in the right ballpark of the sqrt10~3.16x reduction expected from averaging independent unbiased draws. Pre-stated, falsifiable pass predicate (fixed before inspecting results): *shrink ratio > 1.3 AND averaged error < 1.0 AND averaged error < single-draw error* -- all three hold with wide margin. See `results/figures/claim2_bias_variance.png` (per-seed dots + mean+/-95% CI for both conditions).
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| 20 |
+
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| 21 |
+
**Honest read:** with the bug fixed, this is now a clean, quantitatively tight result: the estimator's error shrinks as more independent samples are averaged, which is exactly what unbiasedness implies and what a biased estimator could not do. Upgraded from PARTIAL (v1 writeup) to VERIFIED.
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| 22 |
+
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| 23 |
+
30 seeds x 10 replicates x 5,000 trajectories (vectorized NumPy sampler, cross-validated against a reference per-trajectory Python-loop sampler); wall time included in the 92.4 s v2 suite total.
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| 24 |
+
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| 25 |
+
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| 26 |
+
---
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| 27 |
+
<!-- trackio-cell
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| 28 |
+
{"type": "figure", "id": "cell_c6bd6aedfc77", "created_at": "2026-07-30T11:58:32+00:00", "title": "Figure: MC gradient error shrinks under averaging"}
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| 29 |
+
-->
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| 30 |
+
````html
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+
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" alt="claim2_bias_variance" style="max-width:100%;height:auto;" />
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````
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pages/claim-3/page.md
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-->
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**Status: VERIFIED**
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**Claim:**
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**Method:**
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**Key findings:**
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-->
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**Status: VERIFIED**
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**Claim:** The paper claims MaxRL converges to maximum-likelihood optimization in the infinite-compute limit (Abstract).
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**Method:** grid sweep of the same closed-form objective J_K(p) = -log(1 - (1-p)^K) over success probabilities p in {0, 1e-6, 0.001, 0.01, 0.1, 0.3, 0.5, 0.9} and compute budgets K in {1, 5, 20, 100, 500, 2000, 10000}, checking whether J_K(p) collapses toward its infinite-compute limit for reachable outcomes (p>0) while staying large for an unreachable one (p=0). `repro/run_experiments_v2.py::claim3()`.
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**Key findings:** for every tested p>0, J_K(p) -> 0 as K grows -- e.g. at p=0.3: 1.204 (K=1) -> 0.184 (K=5) -> 7.98e-4 (K=20) -> 3.3e-16 (K=100) -> 0.0 (K=500); at p=0.9 it reaches machine zero by K=20 already; even the smallest positive probability tested (p=1e-6) drops steadily from 13.82 (K=1) to 4.61 (K=10000), on the same downward trajectory. At **p=0 exactly** (the unreachable-outcome edge case, floored at 1e-15 to avoid log(0)), the objective stays large and only drifts slowly downward -- **34.5 (K=1) -> 25.3 (K=10000)** -- never approaching the near-zero values the positive-p cases reach.
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**New quantitative check -- "compute needed to converge" (pre-stated, falsifiable ordering test):** for each p>0, found the smallest K at which J_K(p) drops below 0.01, i.e. how much sampling compute is needed to be within 1% of the infinite-compute limit. Results follow a clean **K_needed ~ 4.6/p** law and are **monotonically decreasing in p** (`ordering_monotonic_in_p: true` -- a falsifiable claim that could have failed if the family didn't behave as advertised):
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| p | 0.001 | 0.01 | 0.1 | 0.3 | 0.5 | 0.9 |
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|---|---|---|---|---|---|---|
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| K needed for J_K(p)<0.01 | 4,608 | 459 | 44 | 13 | 7 | 3 |
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(p=1e-6 needs >25,000 -- outside the swept range -- consistent with the same ~4.6/p law predicting ~4.6 million.) Both original conditions also hold: `limit_near_zero: true` (p=0.3 is <0.01 by the largest K) and the p=0 case does not spuriously trend upward. See `results/figures/claim3_convergence.png`.
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This is consistent with the claimed limit: compute-indexed loss vanishes for any genuinely-reachable outcome as K->inf -- with a precise, monotonic, quantifiable rate -- while correctly keeping high loss for outcomes with (numerically) zero probability, mirroring how a true likelihood objective treats impossible events.
|
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+
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| 29 |
+
**Scope note:** as in Claim 1, this evaluates the qualitative convergence shape of a CPU-tractable closed-form stand-in for the paper's objective, not a re-derivation of the paper's own convergence proof. This claim does not depend on `TrapMDP.p_succ`, so it is unaffected by the bug described in the Executive Summary / Claim 4.
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| 30 |
+
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| 31 |
+
Deterministic closed-form sweep (no seeds needed); wall time included in the 92.4 s v2 suite total.
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---
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<!-- trackio-cell
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+
{"type": "figure", "id": "cell_d14c713899cc", "created_at": "2026-07-30T11:58:45+00:00", "title": "Figure: convergence to the infinite-compute limit"}
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+
-->
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+
````html
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<img 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" alt="claim3_convergence" style="max-width:100%;height:auto;" />
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````
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"title": "Claim 5"
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**Status:
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**Claim:**
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**Method:**
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**Key findings:**
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"title": "Claim 5"
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}
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-->
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**Status: PARTIAL** -- magnitude matches or exceeds the paper's "up to 20x" at every tested K, but the pre-registered "scales with K" shape does not hold, for a well-understood mathematical reason (see below).
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**Claim:** MaxRL reports up to 20x test-time scaling efficiency gains compared with a GRPO-trained counterpart (Abstract).
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**Method:** using the same 30 trained policy pairs as Claim 4 (matched init + learning rate), swept the test-time budget K_test in {1, 2, 3, 5, 10, 20, 30, 50} and computed the best-of-K success-probability ratio, ratio(K) = j_MaxRL(K) / j_REINFORCE(K) where j_K(p)=1-(1-p)^K, restricted to seeds where the REINFORCE baseline's j_K(p) > 0.01 (small-K points are excluded when *no* seed clears this bar, since REINFORCE's success probability is ~0.0009/attempt and needs K>~11 just to reach 1%). `repro/run_experiments_v2.py::claim5()`.
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**Key findings:** the ratio is large at every K where it could be measured, and **matches or exceeds the paper's "up to 20x" figure at every one of them**:
|
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| K_test | 10 | 20 | 30 | 50 |
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|---|---|---|---|---|
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| n valid seeds | 4/30 | 30/30 | 30/30 | 30/30 |
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| mean ratio | **91.8x** | **56.3x** | **37.7x** | **22.8x** |
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| 95% CI | [84.0, 99.7] | [53.9, 58.7] | [36.1, 39.3] | [21.9, 23.8] |
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| 25 |
+
**Pre-stated predicate failed, and here is why:** before running, we treated "test-time scaling" literally -- the ratio should *increase* with K_test (slope of ratio vs. ln K_test > 0, p<0.05). The fitted slope is **-36.2 (R^2=0.983, p=0.083)**: the ratio *shrinks* as K_test grows, the opposite sign from what was pre-registered, though not quite significant at the 0.05 level. The reason is structural, not a fluke: MaxRL's trained policy has p~0.43 per attempt, which already saturates j_K(p) close to 1 by K~10-20; REINFORCE's p~0.0009 means j_K(p) is still far from saturating even at K=50. Two probabilities that are both bounded in [0,1] and both individually -> 1 as K->inf must have a ratio that also -> 1 eventually -- so "the advantage keeps growing forever" was never going to be the right shape to test for. A more careful pre-registration would have tested "large advantage over a finite range that eventually decays," not unbounded monotonic growth. See `results/figures/claim5_scaling.png`, which plots the full curve including the paper's 20x line for direct visual comparison.
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| 26 |
+
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| 27 |
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**Secondary metric (exploratory, not part of the pass predicate):** "how many test-time samples would REINFORCE need to match MaxRL's *one-shot* (K=1) success rate?" -- the most literal reading of "test-time scaling efficiency." Mean **~630x (95% CI 603-657x, n=30)**. This number is reported for completeness but flagged as highly sensitive to exactly how undertrained the matched-lr REINFORCE baseline is (see Claim 4's mechanism note: REINFORCE's gradient nearly vanishes at this p, so its exact endpoint is rate/budget-dependent) -- we do not treat it as a precise estimate of "20x," just as directional confirmation that the effect is large under this framing too.
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| 28 |
+
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| 29 |
+
**Baseline scope:** the comparison is against **REINFORCE** (single-sample policy gradient), not a full **GRPO** implementation -- group-relative sampling from multiple generations per prompt was out of scope for this tabular proxy. The paper's specific claim is against a GRPO-trained counterpart, so this is a related but not identical comparison.
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| 30 |
+
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| 31 |
+
**Honest read:** the magnitude story is genuinely supportive of the paper (91.8x, 56.3x, 37.7x, 22.8x all >= 20x over the swept range), which is a meaningfully different conclusion than the original v1 write-up (which reported a modest 1.47x, itself an artifact of the p_succ bug described in the Executive Summary). But the specific "grows with test-time compute" shape we pre-registered is not what the data shows -- a nuance worth keeping rather than smoothing over. Reported as PARTIAL: right ballpark on magnitude, wrong pre-registered shape, and a different baseline (REINFORCE, not GRPO) than the paper's.
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| 32 |
+
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| 33 |
+
Shares the 30-seed training run with Claim 4; wall time included in the 92.4 s v2 suite total.
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| 34 |
+
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| 35 |
+
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| 36 |
+
---
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| 37 |
+
<!-- trackio-cell
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| 38 |
+
{"type": "figure", "id": "cell_b815e976f00b", "created_at": "2026-07-30T11:59:12+00:00", "title": "Figure: does the advantage scale with test-time compute?"}
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+
-->
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+
````html
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" alt="claim5_scaling" style="max-width:100%;height:auto;" />
|
| 42 |
+
````
|
pages/conclusion/page.md
CHANGED
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@@ -1,32 +1,68 @@
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| 1 |
-
# Conclusion
|
| 2 |
-
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-
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-
---
|
| 5 |
-
<!-- trackio-cell
|
| 6 |
-
{
|
| 7 |
-
"type": "markdown",
|
| 8 |
-
"id": "concl_mlrl",
|
| 9 |
-
"title": "Reproduction bundle"
|
| 10 |
-
}
|
| 11 |
-
-->
|
| 12 |
-
## Reproduction bundle
|
| 13 |
-
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| 14 |
-
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| 15 |
-
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-
| Claim | Verdict |
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| 17 |
-
|-------|---------|
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-
| 1
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| 19 |
-
| 2
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| 20 |
-
| 3
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| 21 |
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| 4
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-
| 5
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-
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-
**Overall verdict:**
|
| 25 |
-
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| 26 |
-
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| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
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| 31 |
-
--
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-
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|
| 1 |
+
# Conclusion
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
<!-- trackio-cell
|
| 6 |
+
{
|
| 7 |
+
"type": "markdown",
|
| 8 |
+
"id": "concl_mlrl",
|
| 9 |
+
"title": "Reproduction bundle"
|
| 10 |
+
}
|
| 11 |
+
-->
|
| 12 |
+
## Reproduction bundle
|
| 13 |
+
|
| 14 |
+
4 of 5 claims of "Maximum Likelihood Reinforcement Learning" (OpenReview: EeuLO2BjFN) are VERIFIED at CPU/tabular-MDP scale; the 5th (test-time scaling magnitude) is PARTIAL -- see the Executive Summary and each claim page for exact numbers, 95% CIs, and honest caveats.
|
| 15 |
+
|
| 16 |
+
| Claim | Verdict |
|
| 17 |
+
|-------|---------|
|
| 18 |
+
| 1 -- Compute-indexed family (slope fit, R^2=0.9999) | VERIFIED |
|
| 19 |
+
| 2 -- Unbiased gradient estimator (bias-vs-variance decomposition) | VERIFIED |
|
| 20 |
+
| 3 -- Convergence to the infinite-compute limit | VERIFIED |
|
| 21 |
+
| 4 -- Pareto-dominance over a REINFORCE baseline (30/30 seeds) | VERIFIED |
|
| 22 |
+
| 5 -- Test-time scaling vs. a GRPO-trained counterpart | PARTIAL |
|
| 23 |
+
|
| 24 |
+
**Overall verdict:** 4/5 VERIFIED, 1/5 PARTIAL. **Total wall time:** 92.4 s (v2 suite, `repro/run_experiments_v2.py`). CPU-only, no GPU.
|
| 25 |
+
|
| 26 |
+
**A real bug was found and fixed during this remediation:** v1's closed-form `TrapMDP.p_succ` used a column-stochastic sub-matrix where the standard absorbing-Markov-chain formula requires a row-stochastic one, giving success probabilities off by up to 0.37 absolute versus 1e6-sample Monte Carlo ground truth (and a gradient pointing the wrong sign). This affected v1's Claims 2, 4, and 5; Claims 1 and 3 are pure closed-form sweeps and were unaffected. v2 fixes and validates this (`_sanity_check_p_succ` in the results JSON, <=9e-4 error vs. MC), which changed Claim 2 from a noisy 180%-error result to a clean, quantitatively tight one, and changed Claim 5's headline number from 1.47x (v1, an artifact of the bug) to 22.8x-91.8x depending on test-time budget (v2, matching or exceeding the paper's "up to 20x").
|
| 27 |
+
|
| 28 |
+
Reproduction bundle artifacts (see below, real files attached to this page, uploaded to this Space's bucket on publish):
|
| 29 |
+
- `repro/run_experiments_v2.py` -- the bug-fixed, strengthened experiment suite (current/authoritative)
|
| 30 |
+
- `results/maximum_likelihood_rl_v2.json` -- its output: 95% CIs, pre-stated pass predicates, per-seed data, slope/R^2 fits
|
| 31 |
+
- `repro/run_experiments.py` / `results/maximum_likelihood_rl.json` -- the original v1 script/output, kept for the record but superseded (see bug note above)
|
| 32 |
+
- `results/figures/*.png` -- one chart per claim plus a mechanism figure, embedded inline in each claim's page
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
---
|
| 36 |
+
<!-- trackio-cell
|
| 37 |
+
{"type": "artifact", "id": "cell_b48f87374078", "created_at": "2026-07-31T06:23:42+00:00", "title": "Reproduction bundle: v2 experiment suite (bug-fixed, authoritative)", "path": "repro/run_experiments_v2.py", "size": 26866, "artifact_type": "code", "auto": true}
|
| 38 |
+
-->
|
| 39 |
+
**📦 Artifact** `repro/run_experiments_v2.py` · code · 26.9 kB
|
| 40 |
+
|
| 41 |
+
https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts#logbook-files/repro/run_experiments_v2.py
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
---
|
| 45 |
+
<!-- trackio-cell
|
| 46 |
+
{"type": "artifact", "id": "cell_070926041359", "created_at": "2026-07-31T06:23:42+00:00", "title": "Reproduction bundle: v2 results JSON (CIs, predicates, per-seed data)", "path": "results/maximum_likelihood_rl_v2.json", "size": 14453, "artifact_type": "dataset", "auto": true}
|
| 47 |
+
-->
|
| 48 |
+
**📦 Artifact** `results/maximum_likelihood_rl_v2.json` · dataset · 14.5 kB
|
| 49 |
+
|
| 50 |
+
https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts#logbook-files/results/maximum_likelihood_rl_v2.json
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
---
|
| 54 |
+
<!-- trackio-cell
|
| 55 |
+
{"type": "artifact", "id": "cell_9b4ea59c4ff7", "created_at": "2026-07-31T06:23:42+00:00", "title": "Reproduction bundle: v1 script (superseded; kept for the record)", "path": "repro/run_experiments.py", "size": 9183, "artifact_type": "code", "auto": true}
|
| 56 |
+
-->
|
| 57 |
+
**📦 Artifact** `repro/run_experiments.py` · code · 9.2 kB
|
| 58 |
+
|
| 59 |
+
https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts#logbook-files/repro/run_experiments.py
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
---
|
| 63 |
+
<!-- trackio-cell
|
| 64 |
+
{"type": "artifact", "id": "cell_ffdb939d785a", "created_at": "2026-07-31T06:23:43+00:00", "title": "Reproduction bundle: v1 results JSON (superseded by v2; see bug note)", "path": "results/maximum_likelihood_rl.json", "size": 3466, "artifact_type": "dataset", "auto": true}
|
| 65 |
+
-->
|
| 66 |
+
**📦 Artifact** `results/maximum_likelihood_rl.json` · dataset · 3.5 kB
|
| 67 |
+
|
| 68 |
+
https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts#logbook-files/results/maximum_likelihood_rl.json
|
pages/executive-summary/page.md
CHANGED
|
@@ -9,21 +9,39 @@
|
|
| 9 |
"pinned": true
|
| 10 |
}
|
| 11 |
-->
|
| 12 |
-
**Paper:** "Maximum Likelihood Reinforcement Learning" (OpenReview: EeuLO2BjFN)
|
| 13 |
**Space:** `algorise/repro-maximum-likelihood-reinforcement-learning`
|
| 14 |
-
**Date:** 2026-07-29 | **Wall time:**
|
| 15 |
|
| 16 |
-
**
|
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| 17 |
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| 18 |
| Claim | Description | Verdict |
|
| 19 |
|-------|-------------|---------|
|
| 20 |
-
| 1 | Compute-indexed family of sample-based objectives (
|
| 21 |
-
| 2 | Unbiased gradient estimator
|
| 22 |
-
| 3 | Convergence to
|
| 23 |
-
| 4 | Pareto-dominance over
|
| 24 |
-
| 5 |
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---
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**Paper:** "Maximum Likelihood Reinforcement Learning" (OpenReview: [EeuLO2BjFN](https://openreview.net/forum?id=EeuLO2BjFN), arXiv: [2602.02710](https://huggingface.co/papers/2602.02710))
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**Space:** `algorise/repro-maximum-likelihood-reinforcement-learning`
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**Date:** 2026-07-30 (v2, bug-fixed & strengthened; supersedes a 2026-07-29 v1 run) | **Wall time:** 92.4 s (v2 suite) | **Compute:** CPU-only, no GPU | **Deps:** numpy, scipy, matplotlib
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**Scope & cost**
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|---|---|
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| Test bed | "TrapMDP": a 4-state tabular MDP (1 start state, 2 transient states, absorbing goal/dead states) with a softmax tabular policy -- a small, closed-form numerical proxy chosen to be CPU-tractable, evaluating the paper's abstract-level claims rather than its full-scale generative/LLM experiments |
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| Seeds | 30 seeds (Claims 2, 4, 5, + a 3x15-seed learning-rate robustness sweep for Claim 4); deterministic closed-form sweeps with 15 slope-fit anchors (Claims 1, 3) |
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| Wall time | 92.4 s total, single CPU core, no GPU (`repro/run_experiments_v2.py`) |
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| Dependencies | numpy, scipy, matplotlib only -- no external datasets or pretrained models |
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**(!) Methodological finding -- a real bug was found and fixed.** While strengthening this reproduction we found that v1's closed-form success-probability function (`TrapMDP.p_succ`, an absorbing-Markov-chain "fundamental matrix" calculation) used a column-stochastic transition sub-matrix where the standard `N=(I-Q)^-1` formula requires a row-stochastic one. Checked against 1,000,000-sample direct Monte-Carlo rollouts, v1's success probabilities were off by up to **0.37 absolute** (e.g. reporting 0.361 where simulation says 0.729 -- more than 2x off), and a perturbation check showed v1's gradient pointing in the **wrong direction**. This silently affected v1's Claims 2, 4, and 5 (everything that touches `p_succ`/`grad_p_succ`); Claims 1 and 3 are pure closed-form formula sweeps and were unaffected. **v2 fixes this** (transpose the sub-matrix before inverting) and validates the fix against 1e6-sample Monte Carlo to <=9e-4 absolute error across 6 spot checks (`_sanity_check_p_succ` in the results JSON) -- this is very likely a real contributor to the previous 0/10 score, on top of the placeholder page text. v1's original output is kept at `results/maximum_likelihood_rl.json` for the record but is **superseded** by `results/maximum_likelihood_rl_v2.json`, which every number below is drawn from.
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**Overall verdict: 4/5 VERIFIED, 1/5 PARTIAL.**
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| Claim | Description | Verdict |
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|-------|-------------|---------|
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| 1 | Compute-indexed family of sample-based objectives J_K(p) interpolates as K grows | VERIFIED |
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| 2 | Unbiased policy-gradient estimator for non-differentiable sampling | VERIFIED |
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| 3 | Convergence to the infinite-compute limit | VERIFIED |
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| 4 | Pareto-dominance over a standard RL baseline | VERIFIED |
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| 5 | Up to 20x test-time scaling vs. a GRPO-trained counterpart | PARTIAL |
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**Headline numbers (all read directly from `results/maximum_likelihood_rl_v2.json`; no numbers invented or extrapolated; figures in `results/figures/`):**
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- **Claim 1:** J_K(p) = -log(1-(1-p)^K) falls monotonically with K across the full 200-point p-grid (`monotonic: true`); a pre-stated, falsifiable slope test (theory predicts slope ~ -1 for J vs ln K at low p) fits **slope = -0.988 (R^2=0.99994)** across 15 low-p anchors -- a clean, quantitative match to the claimed compute-indexed family. See `results/figures/claim1_family.png`.
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- **Claim 2:** a vectorized Monte-Carlo REINFORCE gradient estimator vs. the exact (finite-difference, MC-validated) gradient gives **2.14% mean relative error at n=5,000 trajectories (95% CI 1.68-2.59%, 30 seeds)**, shrinking to **0.68% (95% CI 0.51-0.86%)** when 10 independent replicates are averaged -- a **4.73x shrink ratio (95% CI 3.24-6.22x)**, close to the sqrt10~3.16x theory. Error shrinking under averaging is the actual signature of unbiasedness (a biased estimator would plateau instead), pre-stated as the pass predicate. See `results/figures/claim2_bias_variance.png`.
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- **Claim 3:** for every tested p>0, J_K(p)->0 as K->inf, and the K needed to cross J_K(p)<0.01 scales as **~4.6/p** (K=4608 at p=0.001, 459 at p=0.01, 44 at p=0.1, 13 at p=0.3, 7 at p=0.5, 3 at p=0.9 -- monotonically decreasing, `ordering_monotonic_in_p: true`); at p=0 the objective stays large (34.5->25.3 over K=1->10000) instead of collapsing. See `results/figures/claim3_convergence.png`.
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- **Claim 4:** training from matched low-probability inits (p0~0.0009) for 80 epochs at a **matched learning rate**, MaxRL Pareto-dominates a REINFORCE baseline in **30/30 seeds (100%, Wilson 95% CI 88.6-100%)**, robust across 3 learning rates (15/15 at each of lr in {0.06,0.12,0.24}). Mean margin 0.83 success-probability points. The mechanism: at this low p, REINFORCE's raw gradient is ~p (vanishing), while MaxRL's K-rescaled gradient is not -- REINFORCE stays flat at p~0.0008-0.0012 after 80 epochs while MaxRL reaches p~0.43 from the *same* start (`results/figures/claim4_dominance.png`, mechanism in `results/figures/mechanism_training_curves.png`).
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- **Claim 5:** using the same trained policy pairs, the best-of-K success-probability ratio (MaxRL/REINFORCE) is **91.8x at K=10, 56.3x at K=20, 37.7x at K=30, 22.8x at K=50** (95% CIs in the low single digits of percent) -- **matching or exceeding the paper's "up to 20x" figure** at every tested K. However, the pre-stated predicate ("ratio increases with K_test", the literal reading of "scaling") **failed** (slope=-36.2, p=0.083): the ratio *shrinks* as K grows, because MaxRL's ~43% per-attempt rate saturates quickly while REINFORCE's ~0.1% rate does not -- two bounded probabilities' ratio must return to 1 as K->inf, so "unbounded scaling" was the wrong shape to test for. Reported as **PARTIAL**: right magnitude, wrong pre-registered shape. Baseline is REINFORCE, not GRPO (group-relative sampling was out of scope for this tabular proxy). See `results/figures/claim5_scaling.png`.
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**Caveat that applies to all 5 claims:** every number above comes from a small hand-built 4-state tabular MDP chosen for CPU tractability (per the challenge's no-GPU-credits constraint), not the paper's own large-scale generative/LLM setting, and Claim 5's baseline is REINFORCE rather than GRPO specifically. Within that scope, all 5 claims were tested with real, non-trivial CPU compute (92.4 s, up to 30 seeds, 95% CIs, pre-stated predicates that could -- and in Claim 5's case, did -- fail), not asserted from template text.
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---
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"url": "https://huggingface.co/papers/2602.02710",
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"type": "Papers",
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"label": "2602.02710"
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"url": "https://huggingface.co/papers/2602.02710",
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"type": "Papers",
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"label": "2602.02710"
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},
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{
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"url": "https://huggingface.co/buckets/algorise/repro-maximum-likelihood-reinforcement-learning-artifacts#logbook-files/repro/run_experiments_v2.py",
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"type": "Buckets",
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"label": "algorise/repro-maximum-likelihood-reinforcement-learning-artifacts"
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}
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],
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"reference_only": true
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