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"schema_version": 2,
"title": "Reproduction: Autoregressive Direct Preference Optimization",
"emoji": "\ud83e\uddee",
"space_id": "codemaivanngu/repro-autoregressive-direct-preference-optimization",
"paper": {
"arxiv_id": "2602.09533",
"openreview_id": "DmeFAFUO6f"
},
"tags": [
"icml2026-repro",
"paper-DmeFAFUO6f"
],
"updated_at": "2026-08-03T11:10:51.875114+00:00",
"root": {
"slug": "index",
"title": "Reproduction: Autoregressive Direct Preference Optimization",
"file": "pages/index.md",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"file": "pages/executive-summary/page.md",
"children": []
},
{
"slug": "claim-1-loss-structure",
"title": "Claim 1: ADPO loss structure",
"file": "pages/claim-1-loss-structure/page.md",
"children": []
},
{
"slug": "claim-2-length-measures",
"title": "Claim 2: token and feedback length measures",
"file": "pages/claim-2-length-measures/page.md",
"children": []
},
{
"slug": "claim-3-reduction-to-dpo",
"title": "Claim 3: reduction to DPO",
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},
{
"slug": "claim-4-math-reasoning",
"title": "Claim 4: mathematical reasoning benchmarks",
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},
{
"slug": "claim-5-alpacaeval",
"title": "Claim 5: AlpacaEval 2",
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},
{
"slug": "claim-6-granularity",
"title": "Claim 6: response granularity",
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},
{
"slug": "conclusion",
"title": "Conclusion",
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