Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization | Paper Citation Record

Observation 7365310a-3fd2-4c91-8c37-fd463320a0db · outbound

This paper cites DeepSeek-V4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, 2026.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization DeepSeek-V4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, 2026

Reference 1

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Observation cd133846-5c22-46e3-81f8-834ca7928f53 · outbound

This paper cites Qwen3 Technical Report.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Qwen3 Technical Report

Reference 2

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Observation d732594f-48e5-4513-b631-3cb6958b0350 · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Kimi K2.5: Visual Agentic Intelligence

Reference 3

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Observation 81c5069c-e91b-4c36-a4f3-c63f21e7e327 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization GLM-5: from Vibe Coding to Agentic Engineering

Reference 4

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Observation 1a63bafd-7342-4889-abcf-deb3614d4e54 · outbound

This paper cites The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

Reference 5

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Observation 0adee1f2-c634-4bcc-936b-efa9db5f12d7 · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:54.163495Z

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Observation b073561a-6b0e-484c-b24f-275b34df8314 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Distilling the Knowledge in a Neural Network

Reference 7

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Observation 527674ce-8624-44b4-8ec2-8cc884a1d5c7 · outbound

This paper cites an unresolved cited work.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Unresolved cited work

Reference 8

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Observation cda1910b-de80-4f2f-9f09-97e572c4c9bc · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization A Survey on Knowledge Distillation of Large Language Models

Reference 9

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Observation f81e3e9e-4a8e-4a08-86bf-c0fb9218fe01 · outbound

This paper cites OpenThoughts: Data Recipes for Reasoning Models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization OpenThoughts: Data Recipes for Reasoning Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:54.664811Z

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Observation d5a86de0-e4b1-4f4a-ab34-0afde5bdb373 · outbound

This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization On-policy distillation of language models: Learning from self-generated mistakes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:54.798840Z

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Observation fdaad184-6460-44df-b9c0-ad2ca4e56ba6 · outbound

This paper cites Minillm: Knowledge distillation of large language models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Minillm: Knowledge distillation of large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:54.890205Z

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Observation 8a5326e0-a48e-4338-b044-6408ba749207 · outbound

This paper cites Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.005940Z

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Observation 272defbe-fce9-4c99-8b44-7479c8f90254 · outbound

This paper cites Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents

Reference 14

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Observation ad52218a-d094-48cc-952d-aa95dcbbd88e · outbound

This paper cites On-policy distillation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization On-policy distillation

Reference 15

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Observation f5cdebac-d1de-45e6-9fb7-be2a05d6d840 · outbound

This paper cites BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation

Reference 16

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Observation 51619f70-3fcf-447a-ad0a-4b3ac0ab41f9 · outbound

This paper cites Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.300776Z

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Observation ca6ac455-a2cd-4ba8-aa96-7ad343eeced6 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Neural Machine Translation of Rare Words with Subword Units

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.353518Z

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Observation 6fb29719-9e50-4869-8026-579ac0f0fa82 · outbound

This paper cites How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.425532Z

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Observation 61d92b1d-ed4a-45c7-bb69-b3e5b8ca639c · outbound

This paper cites Knowledge fusion of large language models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Knowledge fusion of large language models

Reference 20

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Observation 8613df07-739b-43dd-b405-1fc16399a90c · outbound

This paper cites Towards cross-tokenizer distillation: the universal logit distillation loss for llms.Transactions on Machine Learning Research (TMLR), 2024.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Towards cross-tokenizer distillation: the universal logit distillation loss for llms.Transactions on Machine Learning Research (TMLR), 2024

Reference 21

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Observation bf2922a7-ccca-4997-a30b-d8fe8f6cbf35 · outbound

This paper cites Dual-space knowledge distillation for large language models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Dual-space knowledge distillation for large language models

Reference 22

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Observation 10b8f234-55a3-46d7-b704-aea56a737970 · outbound

This paper cites Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models

Reference 23

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Observation 0743f326-d772-4821-a0d3-8dc764e0d8e9 · outbound

This paper cites Universal cross-tokenizer distillation via approximate likelihood matching.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Universal cross-tokenizer distillation via approximate likelihood matching

Reference 24

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Observation f13018a2-51e7-4c16-b525-ed7205e2ef1c · outbound

This paper cites Unlocking on-policy distillation for any model family.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Unlocking on-policy distillation for any model family

Reference 25

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Observation bb2ee1b5-0e33-4e06-b1bf-8d1bf51b2bc3 · outbound

This paper cites SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.790864Z

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Observation b0df1043-0977-4429-9a4c-38758d5fdf71 · outbound

This paper cites CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

Reference 27

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Observation 7eb94844-1438-4a36-896f-bf3b19ecfd0a · outbound

This paper cites ByT5: Towards a token-free future with pre-trained byte-to-byte models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization ByT5: Towards a token-free future with pre-trained byte-to-byte models

Reference 28

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Observation 88ffc778-30f8-497c-b9b9-12c6a14425af · outbound

This paper cites MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:55.974479Z

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Observation 0799d3ef-482e-4656-ad44-e5616d0dca69 · outbound

This paper cites Byte Latent Transformer: Patches Scale Better Than Tokens.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:56.046733Z

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Observation 5e3a80e7-f55b-4e8f-8fe7-d341e0f8bbeb · outbound

This paper cites FitNets: Hints for thin deep nets.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization FitNets: Hints for thin deep nets

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Observation e9ed172f-9016-49fd-810c-348419d1b448 · outbound

This paper cites Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes

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Observation fa2c8231-2f81-4080-b0bc-d11294536dc0 · outbound

This paper cites Teaching Small Language Models to Reason.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Teaching Small Language Models to Reason

Reference 33

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Observation ab4e288a-da65-491c-bd02-cdaba873ad08 · outbound

This paper cites MiniPLM: Knowledge Distillation for Pre-Training Language Models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization MiniPLM: Knowledge Distillation for Pre-Training Language Models

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Observation c0bcfb11-c700-4e58-b363-fce44f7f7460 · outbound

This paper cites UI-Genie: A self-improving approach for iteratively boosting MLLM-based mobile GUI agents.Advances in Neural Information Processing Systems, 38:150376–150411, 2025.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization UI-Genie: A self-improving approach for iteratively boosting MLLM-based mobile GUI agents.Advances in Neural Information Processing Systems, 38:150376–150411, 2025

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Observation f83b293e-534a-4639-9eb0-8e576f0e6f5e · outbound

This paper cites UI-Mem: Self-evolving experience memory for online reinforcement learning in mobile GUI agents.arXiv preprint arXiv:2602.05832, 2026.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization UI-Mem: Self-evolving experience memory for online reinforcement learning in mobile GUI agents.arXiv preprint arXiv:2602.05832, 2026

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Observation a2658691-8d2b-48c8-8943-49620fac2b62 · outbound

This paper cites Autoregressive Knowledge Distillation through Imitation Learning.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Autoregressive Knowledge Distillation through Imitation Learning

Reference 37

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Observation 30c9e008-8908-450b-ab97-922d712edb80 · outbound

This paper cites Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 38

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Observation d44d2cca-4c3d-458d-a8bd-74958138efe8 · outbound

This paper cites f-Divergence Minimization for Sequence-Level Knowledge Distillation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization f-Divergence Minimization for Sequence-Level Knowledge Distillation

Reference 39

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Observation 1488b820-6824-4687-954a-28e0b376173d · outbound

This paper cites DistiLLM: Towards Streamlined Distillation for Large Language Models.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization DistiLLM: Towards Streamlined Distillation for Large Language Models

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Observation 3d6f254f-e089-4adb-9c95-cab409fa0e1f · outbound

This paper cites DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs

Reference 41

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Observation 657b184c-c03f-4705-abdc-5f2e6df97b51 · outbound

This paper cites Kat-coder-v2 technical report.arXiv preprint arXiv:2603.27703, 2026.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Kat-coder-v2 technical report.arXiv preprint arXiv:2603.27703, 2026

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Observation 8105933d-2a93-4051-9f85-ec9be90217da · outbound

This paper cites KAT-Coder-V2.5 Technical Report.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization KAT-Coder-V2.5 Technical Report

Reference 43

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Observation cc0f357e-8c9d-4fec-afe6-be75c3e4bd2d · outbound

This paper cites Zero-shot tokenizer transfer.arXiv preprint arXiv:2405.07883, 2025.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Zero-shot tokenizer transfer.arXiv preprint arXiv:2405.07883, 2025

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Observation b9bb61d4-4cc3-4a03-a2d2-35d7922e6dd9 · outbound

This paper cites Breaking the ceiling of the LLM community by treating token generation as a classification for ensembling.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Breaking the ceiling of the LLM community by treating token generation as a classification for ensembling

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Observation 22109fd3-12b5-4ee2-8a50-58873c3837ea · outbound

This paper cites Bridging the gap between different vocabularies for LLM ensemble.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Bridging the gap between different vocabularies for LLM ensemble

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Observation f447a65a-a3e8-4bb8-a499-dce8ee665cc4 · outbound

This paper cites Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling

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Observation eb76f324-2e42-4a7f-8292-0e5c48251517 · outbound

This paper cites Qwen3.5-2b.https://huggingface.co/Qwen/Qwen3.5-2B, 2026.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Qwen3.5-2b.https://huggingface.co/Qwen/Qwen3.5-2B, 2026

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Observation e901e7a8-151d-4e43-b55e-687384cad144 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

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Observation f3e9f124-8704-4427-bcee-340c8f19b253 · outbound

This paper cites GLM-Z1-9B-0414: Model card.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization GLM-Z1-9B-0414: Model card

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Observation c1398cfc-8931-4562-a8a6-8a2d1b78acd4 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization DAPO: An Open-Source LLM Reinforcement Learning System at Scale

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Observation ce7a54d4-7973-4358-91b8-0cf20ab783bf · outbound

This paper cites TACO: Topics in Algorithmic COde generation dataset.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization TACO: Topics in Algorithmic COde generation dataset

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Observation ec07b375-9a8d-4024-bb0c-4d8cabad7f3f · outbound

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

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Measuring mathematical problem solving with the math dataset

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Observation 3626d464-d741-4a68-9cec-713a2f01c44d · outbound

This paper cites Let’s verify step by step.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Let’s verify step by step

Reference 54

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Observation 0deacad6-a97a-4098-9d60-3100c1b5395f · outbound

This paper cites MathArena: Evaluating LLMs on uncontaminated math competitions.https://matharena.ai/, 2025.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization MathArena: Evaluating LLMs on uncontaminated math competitions.https://matharena.ai/, 2025

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Observation 31d403b7-e71b-4d42-85c5-8ca99a464a83 · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

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Observation 2df03126-c2c6-4b6f-aab6-5f2f0fc4a179 · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Livecodebench: Holistic and contamination free evaluation of large language models for code

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Observation 2733ff5c-365e-48b8-ae58-246c8ada2964 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Evaluating Large Language Models Trained on Code

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:58.839779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b5f5768-87a2-432a-88c7-92682d12d1ef · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:58.972025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24bd331e-ab2e-4fc7-b682-51c81dd77a38 · outbound

This paper cites KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Reference 60

Resolution: malformed identifier; no resolver link, observed 2026-08-01T05:09:59.059091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

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Outbound references

Pith citing papers