Paper Citation Record · LEDGER

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs

As of 7 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:2507.03253.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.03253 v2

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:21:57.501475Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T07:01:44.900291Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T05:33:58.451084Z

Reference resolution

68 of 68 outbound references displayed

verified exact3
verified fuzzy17
unresolved46
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7450d016-0969-4188-afac-599e12bcdb68 · outbound

This paper cites GPT-4 Technical Report.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 79574b7c-16fa-4f40-8ca6-671d9b6310ec · outbound

This paper cites an unresolved cited work.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Unresolved cited work

Reference 2

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Observation 8fa02c83-c199-41cc-879a-1af021779bf1 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs The claude 3 model family: Opus, sonnet, haiku

Reference 3

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Observation d44f2571-9608-44ad-848e-a9abfb514dac · outbound

This paper cites Program Synthesis with Large Language Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Program Synthesis with Large Language Models

Reference 4

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Observation 2a620327-9de0-4ad6-8008-923297ebb377 · outbound

This paper cites A critical analysis of the largest source for generative ai training data: Common crawl.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs A critical analysis of the largest source for generative ai training data: Common crawl

Reference 5

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Observation 53b6e27e-e9fe-4b74-b5df-c96e9cf58780 · outbound

This paper cites Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

Reference 6

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Source-reported events for the cited work

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Observation 90a0aa22-8055-4f12-8749-da15131acabe · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Piqa: Reasoning about physical commonsense in natural language

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:49.980601Z

Source-reported events for the cited work

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Observation 7f531adb-7629-4bee-b65c-538d2d47463b · outbound

This paper cites On the resemblance and containment of documents.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs On the resemblance and containment of documents

Reference 8

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Observation f50b8dbe-fa86-489f-8281-d08ba7434a6b · outbound

This paper cites Evaluating Large Language Models Trained on Code.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Evaluating Large Language Models Trained on Code

Reference 9

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Source-reported events for the cited work

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Observation 8c03c560-6257-464d-a824-1f44df122b5a · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 10

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Observation 1f456238-d246-4437-b4f1-387d691d94ce · outbound

This paper cites Flash A ttention-2: Faster attention with better parallelism and work partitioning.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Flash A ttention-2: Faster attention with better parallelism and work partitioning

Reference 11

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Observation ca42f1cc-ecfb-413a-946a-75339c94f4d8 · outbound

This paper cites Sailor: Open Language Models for South-East Asia.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Sailor: Open Language Models for South-East Asia

Reference 12

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Observation 00a45189-c2c1-48c3-98c3-a66350e3491d · outbound

This paper cites The Llama 3 Herd of Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs The Llama 3 Herd of Models

Reference 13

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Observation b117b13d-bb9a-4808-8711-8260ab07d018 · outbound

This paper cites Minedojo: Building open-ended embodied agents with internet-scale knowledge.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Minedojo: Building open-ended embodied agents with internet-scale knowledge

Reference 14

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Observation 7dc05f53-411e-40c0-bacc-be4be248d23d · outbound

This paper cites Lighteval: A lightweight framework for llm evaluation, 2023.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Lighteval: A lightweight framework for llm evaluation, 2023

Reference 15

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Observation 940cbd60-b228-40c5-aa52-27d22bb6a829 · outbound

This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs InCoder: A Generative Model for Code Infilling and Synthesis

Reference 16

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Observation d0b5aa86-d09c-4d75-bbd7-699cf929be26 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Measuring mathematical problem solving with the math dataset

Reference 17

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Observation b30ca208-ed28-44c2-82af-2ae001040afb · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, 2023.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, 2023

Reference 18

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Observation fc1bd634-0ab6-49a8-9a90-94e64049b0bb · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Gonzalez, Hao Zhang, and Ion Stoica

Reference 19

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Observation be8874e2-b0a5-42da-9f9e-cffd68cd07d8 · outbound

This paper cites Rho-1: Not All Tokens Are What You Need.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Rho-1: Not All Tokens Are What You Need

Reference 20

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Observation 165cd0f0-ee77-4662-8d89-b2ee17be86e4 · outbound

This paper cites Efficient Inference for Large Reasoning Models: A Survey.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Efficient Inference for Large Reasoning Models: A Survey

Reference 21

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Observation 176c9530-0d86-42cd-81b3-64832d10da35 · outbound

This paper cites OpenELM: An Efficient Language Model Family with Open Training and Inference Framework.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 22

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Observation 8bebc2c8-8413-4360-9d74-d46b248dce4f · outbound

This paper cites SLANG: New Concept Comprehension of Large Language Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs SLANG: New Concept Comprehension of Large Language Models

Reference 23

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Observation f6c9158a-dbfe-4a3a-9b99-50336ebd57b9 · outbound

This paper cites Introducing meta llama 3: The most capable openly available llm to date, 2024.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Introducing meta llama 3: The most capable openly available llm to date, 2024

Reference 24

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Observation 9ae6e40a-e297-4276-b15c-456dbc3e33c2 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 25

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Observation aa198cee-3b96-4822-b4c5-75c112b9d7d1 · outbound

This paper cites Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception

Reference 26

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Observation 5b978836-5c46-4da4-a3f6-08f365f46dad · outbound

This paper cites Generative agents: Interactive simulacra of human behavior.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Generative agents: Interactive simulacra of human behavior

Reference 27

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Observation 3be0ad10-5659-45d7-85d6-078a19e0f096 · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 28

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Observation 3b214c50-b851-4640-a7d8-d2f1d12bb48b · outbound

This paper cites Datatrove: large scale data processing, 2024 b.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Datatrove: large scale data processing, 2024 b

Reference 29

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Observation ef8dcdae-7b02-4ae4-b247-3dc043503be2 · outbound

This paper cites The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only

Reference 30

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Observation e2972caf-9d02-45b9-b472-b5c476447db3 · outbound

This paper cites DataMan: Data Manager for Pre-training Large Language Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs DataMan: Data Manager for Pre-training Large Language Models

Reference 31

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This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 32

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 33

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Observation 9babaecc-3f96-47d9-8570-8ce6e469ea9c · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Web data mining with organized contents using naive bayes algorithm

Reference 34

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Observation 4fea4167-8318-4f55-8c65-2fc992a212fc · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Winogrande: An adversarial winograd schema challenge at scale

Reference 35

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Observation de97d225-d551-42fc-9daf-9ec83628ca63 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs SocialIQA: Commonsense Reasoning about Social Interactions

Reference 36

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Observation 75c4317c-ccea-4174-a1ff-5c9aa60e5590 · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Toolformer: Language models can teach themselves to use tools

Reference 37

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Observation 9621e435-1282-4009-87e8-92e5d1623dc4 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 38

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs SlimPajama: A 627B token cleaned and deduplicated version of RedPajama , June 2023

Reference 39

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Observation f9a3b0d5-5a17-4bd4-a6da-0334819f2b0c · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Dolma: an open corpus of three trillion tokens for language model pretraining research

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:21:59.545747Z

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Observation 34c86beb-14da-4524-bff9-e9b876b99569 · outbound

This paper cites Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset

Reference 41

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs C ommonsense QA : A question answering challenge targeting commonsense knowledge

Reference 42

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Redpajama: an open dataset for training large language models, October 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:21:59.401589Z

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Observation 0e9793d1-3fec-4858-a7bf-134458de5c9b · outbound

This paper cites M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:21:59.219332Z

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Observation 8738f8af-7e15-4c73-8233-0a045088789e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs LLaMA: Open and Efficient Foundation Language Models

Reference 45

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 46

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Observation c47027db-4378-4261-9fd5-3943afbc1431 · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Code Llama: Open Foundation Models for Code

Reference 47

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Chain-of-thought prompting elicits reasoning in large language models

Reference 48

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Observation 7f417b6c-ba9a-4484-8601-42c4466c8828 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Crowdsourcing Multiple Choice Science Questions

Reference 49

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Observation 594b4f42-1bf6-46a6-9157-bd1f6a7078d3 · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs QuRating : Selecting high-quality data for training language models

Reference 50

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Observation c19a7082-0c5e-4986-893e-1ed92657c4fb · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Data selection for language models via importance resampling

Reference 51

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Observation 692d263e-4ffd-4a57-a9c3-087be751aa98 · outbound

This paper cites Qwen3 Technical Report.

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Qwen3 Technical Report

Reference 52

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs React: Synergizing reasoning and acting in language models

Reference 53

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Craw4LLM: Efficient Web Crawling for LLM Pretraining

Reference 54

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models

Reference 55

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs A normalized levenshtein distance metric

Reference 56

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Reference 57

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

Reference 58

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Reference 59

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Reference 60

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Pytorch fsdp: Experiences on scaling fully sharded data parallel

Reference 61

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale

Reference 62

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Observation d743a0a9-bce4-4c02-ad0f-4ad39613fae5 · outbound

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs Toolqa: A dataset for llm question answering with external tools

Reference 63

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 64

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RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs write newline

Reference 65

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Reference 66

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Reference 67

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Reference 68

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Pith citing papers

Observation a503399e-fa32-4421-87f0-e1c0935992fe · inbound

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools cites this paper.

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs

Reference 10

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Observation 4168c4a2-291d-43a3-a6fd-acef3dc18f4c · inbound

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data cites this paper.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs

Reference 14

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