| { |
| "schema_version": 1, |
| "title": "Reproduction: Distributed Direct Preference Optimization", |
| "emoji": "🎯", |
| "space_id": "SabaPivot/repro-distributed-direct-preference-optimization", |
| "paper": { |
| "arxiv_id": "2605.20696" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-ljNZyrAlaa" |
| ], |
| "updated_at": "2026-08-03T02:07:27.692915+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Distributed Direct Preference Optimization", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1", |
| "title": "Claim 1: Theorem 5.1 gives the first convergence bound for Federated DPO (FedDPO) under partial client participation, showing gradient-norm error scaling with local steps E, rounds R, sampled clients S, and gradient variance ζ²_g (Theorem 5.1).", |
| "file": "pages/claim-1/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2", |
| "title": "Claim 2: Corollary 5.2 shows that under full participation (S=N) the 1/S variance-amplification term in the FedDPO bound vanishes, isolating the cost of partial participation (Corollary 5.2).", |
| "file": "pages/claim-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3", |
| "title": "Claim 3: Theorem 5.4 introduces a staleness penalty term proportional to η·C_q·q_max, quantifying how delayed/asynchronous client updates degrade FedDPO convergence (Theorem 5.4).", |
| "file": "pages/claim-3/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4", |
| "title": "Claim 4: Theorem 5.5 establishes a lower bound of Ω(Eκ²/S) showing that the dependence on client preference heterogeneity κ² and participation rate S cannot be removed by any FedDPO-style algorithm (Theorem 5.5).", |
| "file": "pages/claim-4/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5", |
| "title": "Claim 5: Theorem 6.1 proves DecDPO (decentralized DPO) converges at rate O(1/√R + 1/(R(1−ρ²))) where ρ is the spectral gap of the communication graph, with variance and heterogeneity terms scaled by 1/(1−ρ²) (Theorem 6.1).", |
| "file": "pages/claim-5/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6", |
| "title": "Claim 6: Numerical experiments on the Stanford Human Preferences dataset with N=5 agents empirically confirm the predicted effects of local step count, participation rate, staleness, and network topology on convergence (Section 7, Numerical Results).", |
| "file": "pages/claim-6/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 3400, |
| "revision": "1784656265000000000", |
| "evidence_provenance": "PROVENANCE.md" |
| } |
