{
  "schema_version": 1,
  "title": "Reproduction: High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator",
  "emoji": "🎯",
  "space_id": "SabaPivot/repro-high-dimensional-learning-dynamics-of-quantized-models-with-straight-through-estimator",
  "paper": null,
  "tags": [
    "icml2026-repro",
    "paper-bI9moH3UZw"
  ],
  "updated_at": "2026-07-29T03:33:14.546963+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "executive-summary",
        "title": "Executive summary",
        "file": "pages/executive-summary/page.md",
        "children": []
      },
      {
        "slug": "claim-1-microscopic-sde-limit",
        "title": "Claim 1: In the high-dimensional limit, the microscopic dynamics of straight-through estimator (STE) training converge to a stochastic differential equation governed by Eq. (15) (Section IV, Theorem IV.3).",
        "file": "pages/claim-1-microscopic-sde-limit/page.md",
        "children": []
      },
      {
        "slug": "claim-2-macroscopic-ode-concentration",
        "title": "Claim 2: The macroscopic summary statistics of STE training concentrate onto a deterministic ODE trajectory (Eqs. (24)-(25)) with concentration rate O_d(d^{-1/2}) as dimension d grows (Section V-B, Theorem V.3).",
        "file": "pages/claim-2-macroscopic-ode-concentration/page.md",
        "children": []
      },
      {
        "slug": "claim-3-plateau-and-sharp-drop",
        "title": "Claim 3: STE training exhibits an extended plateau followed by a sharp drop in generalization error across bit-widths b in {2,3,4,5}, with the plateau length depending on the quantization range ω (Section VI-A1/VI-A2, Figures 2-3).",
        "file": "pages/claim-3-plateau-and-sharp-drop/page.md",
        "children": []
      },
      {
        "slug": "claim-4-input-only-stable-fixed-point",
        "title": "Claim 4: For input-only quantization, a closed-form stable fixed point exists under the learning-rate condition 0 < η < 2(σ_ψ^2 + λ)/σ_ψ^4 (Section VI-C1, Proposition VI.1).",
        "file": "pages/claim-4-input-only-stable-fixed-point/page.md",
        "children": []
      },
      {
        "slug": "claim-5-three-regime-asymptotic-deviation",
        "title": "Claim 5: The asymptotic deviation of the STE-trained quantized model from the unquantized linear model is characterized across three regimes, with a leading-order correction term of size O_Δ(Δ^2) depending on the fractional quantization level position (Section VI-C2, Theorem VI.3).",
        "file": "pages/claim-5-three-regime-asymptotic-deviation/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  },
  "agent_view_tokens": 942,
  "revision": "1784621300485295000"
}