| { |
| "schema_version": 1, |
| "title": "Reproduction: Maximum Likelihood Reinforcement Learning", |
| "emoji": "🎯", |
| "space_id": "Umong/repro-maximum-likelihood-reinforcement-learning", |
| "paper": { |
| "arxiv_id": "2602.02710" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-EeuLO2BjFN" |
| ], |
| "updated_at": "2026-07-18T18:54:07+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Maximum Likelihood Reinforcement Learning", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-maxrl-defines-a-compute-indexed-family-of-sample-based-objectives-that-interpolates-between-standard-rl-and-exact-maximum-likelihood-as-sampling-compute-increases-abstract", |
| "title": "Claim 1: MaxRL defines a compute-indexed family of sample-based objectives that interpolates between standard RL and exact maximum likelihood as sampling compute increases (Abstract).", |
| "file": "pages/claim-1-maxrl-defines-a-compute-indexed-family-of-sample-based-objectives-that-interpolates-between-standard-rl-and-exact-maximum-likelihood-as-sampling-compute-increases-abstract/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-the-maxrl-objectives-admit-a-simple-unbiased-policy-gradient-estimator-for-non-differentiable-sampling-settings-abstract", |
| "title": "Claim 2: The MaxRL objectives admit a simple unbiased policy-gradient estimator for non-differentiable sampling settings (Abstract).", |
| "file": "pages/claim-2-the-maxrl-objectives-admit-a-simple-unbiased-policy-gradient-estimator-for-non-differentiable-sampling-settings-abstract/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-the-paper-claims-maxrl-converges-to-maximum-likelihood-optimization-in-the-infinite-compute-limit-abstract", |
| "title": "Claim 3: The paper claims MaxRL converges to maximum-likelihood optimization in the infinite-compute limit (Abstract).", |
| "file": "pages/claim-3-the-paper-claims-maxrl-converges-to-maximum-likelihood-optimization-in-the-infinite-compute-limit-abstract/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-empirically-maxrl-pareto-dominates-tested-existing-methods-across-all-evaluated-models-and-tasks-abstract", |
| "title": "Claim 4: Empirically, MaxRL Pareto-dominates tested existing methods across all evaluated models and tasks (Abstract).", |
| "file": "pages/claim-4-empirically-maxrl-pareto-dominates-tested-existing-methods-across-all-evaluated-models-and-tasks-abstract/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-maxrl-reports-up-to-20x-test-time-scaling-efficiency-gains-compared-with-a-grpo-trained-counterpart-abstract", |
| "title": "Claim 5: MaxRL reports up to 20x test-time scaling efficiency gains compared with a GRPO-trained counterpart (Abstract).", |
| "file": "pages/claim-5-maxrl-reports-up-to-20x-test-time-scaling-efficiency-gains-compared-with-a-grpo-trained-counterpart-abstract/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 3376, |
| "revision": "1784400847404442706" |
| } |
