Pith Number

pith:S5UAZ64F

pith:2026:S5UAZ64FM5T5LD3SZU6EG2VMZX

not attested not anchored not stored refs resolved

AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions

Dilek Hakkani-T\"ur, Emre Can Acikgoz, Ishika Agarwal, Jiaqi Ma, Mahdi Namazifar, Pradeep Natarajan, Sofia Stoica

Acquisition functions as rewards train models to generate synthetic data that improves student performance by 2-7 percent

arxiv:2605.13149 v1 · 2026-05-13 · cs.CL · cs.AI · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

student models trained with AcquisitionSynthesis data achieve good performance on in-distribution tasks (2-7% gain) and is more robust to catastrophic forgetting

C2weakest assumption

That acquisition functions provide a reliable quantitative signal of a sample's impact on the downstream learner that can be directly used as a reward without further validation or adaptation.

C3one line summary

AcquisitionSynthesis uses acquisition functions as rewards to train generators that produce higher-quality synthetic data, delivering 2-7% gains on math, medical QA, and coding tasks with improved robustness to forgetting.

References

62 extracted · 62 resolved · 2 Pith anchors

[1] Understanding Black-box Predictions via Influence Functions , author=. 2020 , eprint= 2020

[2] DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback , author=. 2025 , eprint= 2025

[3] Xia, Mengzhou and Malladi, Sadhika and Gururangan, Suchin and Arora, Sanjeev and Chen, Danqi , booktitle=

[4] Reflexion: Language Agents with Verbal Reinforcement Learning , author=. 2023 , eprint= 2023

[5] DAPO: An Open-Source LLM Reinforcement Learning System at Scale , author=. 2025 , eprint= 2025

Receipt and verification

First computed	2026-05-18T03:08:57.208072Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

97680cfb856767d58f72cd3c436aaccdc6b06558d9980ce9bd96609ac6e9f8ff

Aliases

arxiv: 2605.13149 · arxiv_version: 2605.13149v1 · doi: 10.48550/arxiv.2605.13149 · pith_short_12: S5UAZ64FM5T5 · pith_short_16: S5UAZ64FM5T5LD3S · pith_short_8: S5UAZ64F

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/S5UAZ64FM5T5LD3SZU6EG2VMZX \
  | 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: 97680cfb856767d58f72cd3c436aaccdc6b06558d9980ce9bd96609ac6e9f8ff

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "744050bae97cfb73e7ba0071880c23b80262a0161883a193b4ac1cb782cf66b0",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-13T08:15:48Z",
    "title_canon_sha256": "2752767d01801f55bda50326841fb7219eaa66a04fc96bf2521df3d9ef580de2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13149",
    "kind": "arxiv",
    "version": 1
  }
}