PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts | Paper Citation Record

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

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts SemDeDup: Data-efficient learning at web-scale through semantic deduplication

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Layer Normalization

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Fair Resource Allocation in Multi-Task Learning

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Bengio, J

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Bradbury, R

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Brown, B

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts PaLM: Scaling Language Modeling with Pathways

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This paper cites Chowdhery, S.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Chowdhery, S

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

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Dac Lai, C

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Scaling Vision Transformers to 22 Billion Parameters

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts The Llama 3 Herd of Models

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Metadata Conditioning Accelerates Language Model Pre-training

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts BiMix: A Bivariate Data Mixing Law for Language Model Pretraining

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Hoffmann, S

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This paper cites Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 58

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Observation 88a8da9a-7b5c-41d3-9d59-266cfc7d4f69 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Gemini: A Family of Highly Capable Multimodal Models

Reference 59

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Observation 447e7f22-6ef8-4866-8fa7-81b1eeffe5ff · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 60

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Observation f1334642-6086-463b-9e70-0259f235dc81 · outbound

This paper cites Vandenhende, S.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Vandenhende, S

Reference 61

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Observation 7fcbe20f-713a-4f59-aad5-012ad183fe53 · outbound

This paper cites Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach

Reference 62

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Observation 86bf22b8-0dc6-4043-a6f1-8ff7c8787b75 · outbound

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Reference 63

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Observation b60273fd-11f9-4a60-a61d-f52a6d896b45 · outbound

This paper cites Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

Reference 64

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Observation 55dfd1c6-7e30-49b7-ace6-f1c89c901824 · outbound

This paper cites QuRating: Selecting High-Quality Data for Training Language Models.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts QuRating: Selecting High-Quality Data for Training Language Models

Reference 65

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Observation 2010af6c-5a83-440d-8144-4115041fd377 · outbound

This paper cites Small-scale proxies for large-scale Transformer training instabilities.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Small-scale proxies for large-scale Transformer training instabilities

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.408373Z

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Observation b18f82e8-ea7b-42dd-b668-4d9ab07d267d · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 67

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Observation 12476dd6-b919-42df-a259-77720843e885 · outbound

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Unresolved cited work

Reference 68

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Observation d3bc8e8d-d6b4-4994-a9f0-578e3199c325 · outbound

This paper cites an unresolved cited work.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Unresolved cited work

Reference 69

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Observation 0bd3df9e-7d8b-44d7-a9ca-df8be45cc50c · outbound

This paper cites mT5: A massively multilingual pre-trained text-to-text transformer.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts mT5: A massively multilingual pre-trained text-to-text transformer

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.426348Z

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Observation 7a1b2ca3-f15d-432c-b82b-4d3bc560187b · outbound

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts ByT5: Towards a token-free future with pre-trained byte-to-byte models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.430971Z

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Unavailable: canonical work link unavailable.

Observation bdeffc12-87f1-4fb0-a7dd-6b16b2c8bc86 · outbound

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Unresolved cited work

Reference 72

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Observation 6573b0bb-16db-45b0-b561-b91f1d432115 · outbound

This paper cites Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Reference 73

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Observation 03962849-f2b2-4453-bb7e-f8d3426bd3a9 · outbound

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PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:39.126374Z

Source-reported events for the cited work

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Observation 39479523-eb42-40a3-8967-a13e0f4595a5 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.449710Z

Source-reported events for the cited work

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Observation faaeb223-ad35-4ee3-baf9-813f5dbef9fe · outbound

This paper cites Why Transformers Need Adam: A Hessian Perspective.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Why Transformers Need Adam: A Hessian Perspective

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.454661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 689bb806-2dd1-40a5-b6c1-2b897d0383f1 · outbound

This paper cites ratio,” which defined as ⟨Lj (θ),Lk(θ)⟩ ∥Lj (θ)∥2+∥Lk(θ)∥2 , between task gradients during language model pre-training over time. “ data1- data2.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts ratio,” which defined as ⟨Lj (θ),Lk(θ)⟩ ∥Lj (θ)∥2+∥Lk(θ)∥2 , between task gradients during language model pre-training over time. “ data1- data2

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T16:20:39.109219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Outbound references

Pith citing papers