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pith:3CF6TPWU

pith:2026:3CF6TPWUXOC2BJKUVWBF2JKX26

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Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning

Fangxin Wang, Henry Peng Zou, Langzhou He, Peyman Baghershahi, Philip S. Yu, Sourav Medya

An optimizer-aware Filter-then-Weight method improves convergence in online LLM fine-tuning by matching updates to the current optimizer state.

arxiv:2604.00001 v2 · 2026-03-08 · cs.LG · cs.AI · cs.CL

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Claims

C1strongest claim

Experiments show that our method consistently improves convergence and downstream performance over existing online data selection baselines under the same data budget.

C2weakest assumption

That the optimizer-aware update-matching formulation correctly captures sample utility and that the two-stage filter-plus-weight procedure can be computed efficiently without introducing new biases for long-context LLM data.

C3one line summary

Filter-then-Weight is a two-stage optimizer-aware method that filters geometrically useful data candidates and optimizes their coefficients to shape target updates in online LLM fine-tuning.

References

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[1] arXiv preprint arXiv:2308.03296 , year=

[2] From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning 2024

[3] Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, and Xing Sun 2020

[4] Tagcos: Task- agnostic gradient clustered coreset selection for instruction tuning data 2025

[5] As discussed in Section 4.4, the top-k filtering based can achieve comparable peak performance, with little increased cost compared with filter-only methods 2020

Cited by

1 paper in Pith

Let the Target Select for Itself: Data Selection via Target-Aligned Paths

Receipt and verification

First computed	2026-05-18T03:10:03.368690Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d88be9bed4bb85a0a554ad825d2557d7a8cd03f7ac0ff01b41054b5386f7566c

Aliases

arxiv: 2604.00001 · arxiv_version: 2604.00001v2 · doi: 10.48550/arxiv.2604.00001 · pith_short_12: 3CF6TPWUXOC2 · pith_short_16: 3CF6TPWUXOC2BJKU · pith_short_8: 3CF6TPWU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3CF6TPWUXOC2BJKUVWBF2JKX26 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d88be9bed4bb85a0a554ad825d2557d7a8cd03f7ac0ff01b41054b5386f7566c

Canonical record JSON

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    "abstract_canon_sha256": "f90620ab9f73f6ef880cec6e7db56e159c3020357f0c959f11f48893daa59db5",
    "cross_cats_sorted": [
      "cs.AI",
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    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-08T21:46:16Z",
    "title_canon_sha256": "1b5148e548e6b4015b9c36280d09946883ff2a59ef212dc0770c29192d5c4b8c"
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  "source": {
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    "kind": "arxiv",
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}