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"schema_version": 1,
"title": "Repro - Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization",
"emoji": "🎯",
"space_id": "batesm/repro-dmpo",
"paper": {
"arxiv_id": "2510.08233"
},
"tags": [
"icml2026-repro",
"paper-09CSjVeDug"
],
"updated_at": "2026-07-15T16:54:55+00:00",
"root": {
"slug": "index",
"title": "Repro - Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization",
"file": "pages/index.md",
"children": [
{
"slug": "claim-1-dmpo-distribution-matching-via-cross-entropy",
"title": "Claim 1: DMPO distribution matching via cross-entropy",
"file": "pages/claim-1-dmpo-distribution-matching-via-cross-entropy/page.md",
"children": []
},
{
"slug": "claim-2-weight-baseline-subtraction-for-small-batch-training",
"title": "Claim 2: Weight baseline subtraction for small-batch training",
"file": "pages/claim-2-weight-baseline-subtraction-for-small-batch-training/page.md",
"children": []
},
{
"slug": "claim-3-r1-zero-like-recipe-without-sft",
"title": "Claim 3: R1-Zero-like recipe without SFT",
"file": "pages/claim-3-r1-zero-like-recipe-without-sft/page.md",
"children": []
},
{
"slug": "claim-4-benchmark-comparison-against-baselines",
"title": "Claim 4: Benchmark comparison against baselines",
"file": "pages/claim-4-benchmark-comparison-against-baselines/page.md",
"children": []
},
{
"slug": "claim-5-accuracy-gains-up-to-54-3-over-sota",
"title": "Claim 5: Accuracy gains up to 54.3% over SOTA",
"file": "pages/claim-5-accuracy-gains-up-to-54-3-over-sota/page.md",
"children": []
},
{
"slug": "conclusion",
"title": "Conclusion",
"file": "pages/conclusion/page.md",
"children": []
}
]
},
"agent_view_tokens": 3747,
"revision": "1784134495250998000"
} |