From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning | Paper Citation Record

Observation 4715e4f1-1c65-4fc2-874b-9deef821b6f1 · outbound

This paper cites Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36:55006–55021.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36:55006–55021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.247872Z

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Observation e777c71e-155e-4cd9-969d-ae3ff3e810e4 · outbound

This paper cites What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.244652Z

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Observation befc2ff9-f85f-424c-83d3-8d8789de3a9f · outbound

This paper cites Data-juicer: A one-stop data processing system for large language models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Data-juicer: A one-stop data processing system for large language models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.241532Z

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Observation fd3149c3-4587-4a17-b2fe-1543e656cb1c · outbound

This paper cites LESS: Selecting influential data for targeted instruction tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning LESS: Selecting influential data for targeted instruction tuning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.295769Z

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Observation a17fadca-fe17-4b4e-8dc2-882d30347bcf · outbound

This paper cites Task-specific data selection for instruction tuning via monosemantic neuronal activations.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Task-specific data selection for instruction tuning via monosemantic neuronal activations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.230942Z

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Observation 294bc0fe-01c7-498f-9d0c-838682de0011 · outbound

This paper cites Lead: iterative data selection for efficient llm instruction tuning.Proceedings of the VLDB Endowment, 19(3):426–439.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Lead: iterative data selection for efficient llm instruction tuning.Proceedings of the VLDB Endowment, 19(3):426–439

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.299194Z

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Observation b050ce25-53b4-4aa7-8797-45aab7edd99c · outbound

This paper cites Datachef: Cooking up optimal data recipes for llm adaptation via reinforcement learning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Datachef: Cooking up optimal data recipes for llm adaptation via reinforcement learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:26.686535Z

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Observation c2d65200-50c0-4620-a552-455429877e91 · outbound

This paper cites LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.649702Z

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Observation 97385bba-1145-4265-a1f9-a43d12c021a4 · outbound

This paper cites Evaluating Data Influence in Meta Learning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Evaluating Data Influence in Meta Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:26.676425Z

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Observation 5f818755-12a0-4f34-b09e-6c859bc29ded · outbound

This paper cites From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.227970Z

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Observation fd598a7e-d805-41dd-bd20-75eb731cc494 · outbound

This paper cites Deduplicating training data makes language mod- els better.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Deduplicating training data makes language mod- els better

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.274336Z

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Observation d0531286-0e3e-4904-8937-38e1a997ae38 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:43:31.136179Z

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Observation ee0e6dae-9e7a-4822-a9be-cb7eee2e3804 · outbound

This paper cites Data diversity matters for robust instruction tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Data diversity matters for robust instruction tuning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.283274Z

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Observation 90d40d11-b0af-4a6a-9b3e-94542dcddbcb · outbound

This paper cites Automatic configuration of llm post-training pipelines.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Automatic configuration of llm post-training pipelines

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:26.673700Z

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Observation 2b19e6e7-58dd-47bf-b241-1a1f09fff59c · outbound

This paper cites Random search for hyper-parameter optimization.Journal of machine learning research, 13(2).

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Random search for hyper-parameter optimization.Journal of machine learning research, 13(2)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.233838Z

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Observation 22669bad-b791-46ac-8efe-760da4b1405b · outbound

This paper cites Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.256646Z

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Observation 862ae9fd-0111-4df8-8331-e7397dea5988 · outbound

This paper cites Hy- perband: A novel bandit-based approach to hyperparameter optimization.Journal of Machine Learning Research, 18(185):1–52.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Hy- perband: A novel bandit-based approach to hyperparameter optimization.Journal of Machine Learning Research, 18(185):1–52

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.249180Z

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Observation 7b36ad39-26d3-4aaa-80b4-665cb1f75143 · outbound

This paper cites BOHB: Robust and efficient hyperparameter optimization at scale.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning BOHB: Robust and efficient hyperparameter optimization at scale

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.243145Z

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Observation 69fc331b-c5a4-4f75-acc8-c0d05fda3ae6 · outbound

This paper cites Efficient and robust automated machine learning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Efficient and robust automated machine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.246476Z

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Observation 24698167-87a8-46e8-a31f-b700c0c29721 · outbound

This paper cites Limit: Less is more for instruction tuning across evaluation paradigms.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Limit: Less is more for instruction tuning across evaluation paradigms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.251562Z

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Observation 5f42c2ab-bab2-4895-b2c6-c58a014a8a6a · outbound

This paper cites Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.259050Z

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Observation 4555efc8-c8d5-4bad-83a8-00d125b0a367 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instruc- tions.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Self-instruct: Aligning language models with self-generated instruc- tions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.269020Z

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Observation f1cbe55d-d6b8-488a-888b-56e355e4b465 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.236486Z

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Observation 4a07cdc6-dace-42b1-ae8f-ee819d90cf8a · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Training Verifiers to Solve Math Word Problems

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.665574Z

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Observation 3e94e300-0cdf-4e7c-ab52-8a428333929d · outbound

This paper cites Brown, et al.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Brown, et al

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.240751Z

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Observation 5f023c1c-6244-45cf-95e0-4b75334ec440 · outbound

This paper cites Measuring massive multitask language understanding.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Measuring massive multitask language understanding

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.231248Z

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Observation 16c900b4-423e-4a77-8f9a-0ffe0fa12e59 · outbound

This paper cites Qwen2.5 Technical Report.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Qwen2.5 Technical Report

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.693616Z

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Observation ecfd9b10-dae6-469b-b359-1a0854fe03d0 · outbound

This paper cites The Llama 3 Herd of Models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning The Llama 3 Herd of Models

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.659122Z

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Observation eba0f0c1-2fd9-4a3d-bc44-b12c3d448815 · outbound

This paper cites Graphwiz: An instruction-following language model for graph computational problems.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Graphwiz: An instruction-following language model for graph computational problems

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.180895Z

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Observation 747fbccd-d6a8-4a61-9a9e-1ca5d2ca2e26 · outbound

This paper cites Can language models solve graph problems in natural language?Advances in Neural Informa- tion Processing Systems, 36:30840–30861.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Can language models solve graph problems in natural language?Advances in Neural Informa- tion Processing Systems, 36:30840–30861

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.178662Z

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Observation 68d15f8b-52cd-40d9-8852-050fe7ec2136 · outbound

This paper cites Optimal lossless data compression: Non-asymptotics and asymptotics.IEEE Transactions on Information Theory, 60(2):777–795.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Optimal lossless data compression: Non-asymptotics and asymptotics.IEEE Transactions on Information Theory, 60(2):777–795

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:26.251914Z

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Observation d8847618-2bdf-4e3b-908f-f05435c9c331 · outbound

This paper cites A new generalized varentropy and its properties.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning A new generalized varentropy and its properties

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.226425Z

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Observation d951f526-fd7a-4b6c-bdd1-6fc292e7bc0b · outbound

This paper cites Exploring iterative controllable summarization with large language models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Exploring iterative controllable summarization with large language models

Reference 34

Resolution: metadata mismatch; doi, observed 2026-05-14T20:39:26.244000Z

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Observation d69dfee8-277d-43fa-b9f4-0b455a7ceaf8 · outbound

This paper cites Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.679983Z

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Observation f796cac9-d3c7-4a80-813b-82987f9540ab · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.224115Z

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Observation 5623a6be-2872-446e-9a2b-8c721a572eb7 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.219079Z

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Observation 05f59ea9-39ea-46bb-b6ef-3db85751116e · outbound

This paper cites The best of both worlds: Bridging quality and diversity in data selection with bipartite graph.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning The best of both worlds: Bridging quality and diversity in data selection with bipartite graph

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.221703Z

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Observation 2e84cacb-1aaa-4718-8d9b-11b0f7d4f26f · outbound

This paper cites A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:39:26.713257Z

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Observation 623a51f1-d8e8-47c3-ab8c-3eca2b712dca · outbound

This paper cites Chasing random: Instruction selection strategies fail to generalize.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Chasing random: Instruction selection strategies fail to generalize

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.228883Z

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Observation 6e64941c-a5cc-42a1-864f-11bb86ad213a · outbound

This paper cites Large-Scale Data Selection for Instruction Tuning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Large-Scale Data Selection for Instruction Tuning

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:39:26.646591Z

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Observation d2b9fb81-7db0-4deb-9cb9-a57f072d564c · outbound

This paper cites A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't).

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-06-19T17:11:54.579692Z

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Observation fcecfd25-ccc4-4dca-bd7a-c409106f147f · outbound

This paper cites Smaller language models are capable of selecting instruction-tuning training data for larger language models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Smaller language models are capable of selecting instruction-tuning training data for larger language models

Reference 43

Resolution: verified exact; doi, observed 2026-05-14T20:39:26.250356Z

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Observation bc1cfd25-b92c-4e73-a3da-d5e39504931a · outbound

This paper cites Smalltolarge (s2l): Scalable data selection for fine-tuning large language models by summarizing train- ing trajectories of small models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Smalltolarge (s2l): Scalable data selection for fine-tuning large language models by summarizing train- ing trajectories of small models

Reference 44

Resolution: verified exact; doi, observed 2026-05-14T20:39:26.245598Z

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Observation c33d70ef-6cf9-4f2c-b4b4-fe43da0ef044 · outbound

This paper cites Doremi: Optimiz- ing data mixtures speeds up language model pretraining.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Doremi: Optimiz- ing data mixtures speeds up language model pretraining

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.213941Z

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Observation 7445782c-912e-4882-a0ae-135981f0a295 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:39:26.699806Z

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Observation 9f722457-dfd2-432d-8a2c-8de4b8e1f89c · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Sparse autoencoders find highly interpretable features in language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.208342Z

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Observation d4234ffa-c54c-4592-a19d-5af3e7582f0b · outbound

This paper cites Operators.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Operators

Reference 48

Resolution: malformed identifier; arxiv_id, observed 2026-05-14T20:39:26.652501Z

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Observation 8f1fde34-1829-45fd-b9d2-b30f0d6e357e · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.210590Z

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Observation b9dc190f-84b4-44a8-a1ed-b5366639f008 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.216586Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b9ea0e3-3daa-4b2c-a89d-dae30cf26f63 · outbound

This paper cites Be direct and quantitative.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Be direct and quantitative

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.234021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b91c201f-a11c-4e53-8349-3ba37fc84a94 · outbound

This paper cites Avoid aggressive filtering.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Avoid aggressive filtering

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.264070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0643fd05-47a6-4d51-8ffb-90e12b118fd0 · outbound

This paper cites Try higher rates or skip it.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Try higher rates or skip it

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.288922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 03807b02-0bc6-4a34-ae98-133238f50c1f · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.186805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a76790bd-bf26-4b89-a6ff-30a9bc4ad153 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.190943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6cb88c4b-2863-4350-86a5-e05ce8f5e5de · outbound

This paper cites Do NOT include markdown blocks (‘ ‘‘‘json ‘), just raw JSON.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Do NOT include markdown blocks (‘ ‘‘‘json ‘), just raw JSON

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.196305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 31189e74-084e-4b06-8e65-96b1bf3afea6 · outbound

This paper cites steps": [ {.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning steps": [ {

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.193407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a106b52e-2fd2-462b-8627-72a80eea87f1 · outbound

This paper cites - A candidate whose per-task MONA scores improve across multiple benchmarks is a strong positive signal, even if retain_ratio drops.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning - A candidate whose per-task MONA scores improve across multiple benchmarks is a strong positive signal, even if retain_ratio drops

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.205162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 51528fe8-7c51-45c0-9941-408fc7d18c03 · outbound

This paper cites - Late search: prefer high mu candidates to refine the best.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning - Late search: prefer high mu candidates to refine the best

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.199163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72ebdc65-76a8-4ba3-bac9-ef2dc2048776 · outbound

This paper cites - Historical evidence shows extreme filtering often fails catastrophically.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning - Historical evidence shows extreme filtering often fails catastrophically

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.238613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d80de820-7c54-430c-8d5c-4cdad39d87cb · outbound

This paper cites - Operators from the same family are often redundant.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning - Operators from the same family are often redundant

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.239123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3139e63f-f87a-436e-b0c1-27e8f18e1586 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.182786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 330ba93c-c32e-4f8f-9d47-c5b9f47f31f5 · outbound

This paper cites - High distribution_drift indicates risky distributional shift.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning - High distribution_drift indicates risky distributional shift

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.266339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01657263-269f-4737-bd8c-ca56cbfab960 · outbound

This paper cites ranking": [<best_idx>, <2nd_idx>, ..., <worst_idx>].

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning ranking": [<best_idx>, <2nd_idx>, ..., <worst_idx>]

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.184840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dc25a044-4907-4f9c-89bb-33004a503d30 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.261431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dfe2f6e1-1b38-420d-9fbc-71a25a5ad86b · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.188809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc32d51f-d51e-4be6-aab5-bc13e2b609b2 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.202020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8d69748-f75c-4405-9581-247fa084c6e9 · outbound

This paper cites operator.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning operator

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.271562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5955a55-7ce5-4aad-bd21-075e54ad708b · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.292427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ffc775ef-774d-45fb-82fd-d3ec0d1e3a24 · outbound

This paper cites Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: Which of the following is NOT a function of the cell membrane? A.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: Which of the following is NOT a function of the cell membrane? A

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:25:56.236676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d78b58c-9e81-49b3-8605-eeabe00e656f · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:25:56.225194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc5788b8-ea3a-4eb9-af27-f1d179f60a04 · outbound

This paper cites Example format: [Your reasoning] Answer: (B) Few-shot turns: User: not ( True ) and ( True ) is Assistant: not ( True ) evaluates to False.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Example format: [Your reasoning] Answer: (B) Few-shot turns: User: not ( True ) and ( True ) is Assistant: not ( True ) evaluates to False

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.277417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 196c08b5-f2bb-4a0d-878c-967b6b6456ef · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-05-15T14:20:56.280448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8fbfbd8-801b-4789-9f4c-e8f216058977 · outbound

This paper cites an unresolved cited work.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Unresolved cited work

Reference 76

Resolution: parse uncertain; raw_fallback, observed 2026-05-15T14:20:56.285964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7df6c424-88db-4324-87ab-7cb96ddfa3e0 · outbound

This paper cites Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: What is the capital of France? A.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Example format: [Your reasoning] Answer: B Few-shot turns: User: Question: What is the capital of France? A

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:20:56.253964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers