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Update logbook: Reproduction: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training
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"title": "Reproduction: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training",
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"tags": [
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"updated_at": "2026-08-01T22:38:06+00:00",
"root": {
"slug": "index",
"title": "Reproduction: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training",
"file": "pages/index.md",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"file": "pages/executive-summary/page.md",
"children": []
},
{
"slug": "claim-1-theorem-4-1-spurious-equilibrium",
"title": "Claim 1: Theorem 4.1 β€” spurious parameters at the DPO equilibrium",
"file": "pages/claim-1-theorem-4-1-spurious-equilibrium/page.md",
"children": []
},
{
"slug": "claim-2-theorem-5-3-deployment-suboptimality",
"title": "Claim 2: Theorem 5.3 β€” deployment suboptimality decomposition",
"file": "pages/claim-2-theorem-5-3-deployment-suboptimality/page.md",
"children": []
},
{
"slug": "claim-3-theorem-6-2-tie-training-ratio",
"title": "Claim 3: Theorem 6.2 β€” tie-training reduction ratio",
"file": "pages/claim-3-theorem-6-2-tie-training-ratio/page.md",
"children": []
},
{
"slug": "claim-4-neural-network-spurious-gap",
"title": "Claim 4: Neural network experiments β€” spurious gap / adversarial accuracy",
"file": "pages/claim-4-neural-network-spurious-gap/page.md",
"children": []
},
{
"slug": "claim-5-llm-hotel-preferences",
"title": "Claim 5: LLM synthetic Hotel Preferences benchmark",
"file": "pages/claim-5-llm-hotel-preferences/page.md",
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},
{
"slug": "conclusion",
"title": "Conclusion",
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