Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction | Paper Citation Record

Observation 307caa90-3f6a-435a-a942-7176927e0d8c · outbound

This paper cites GPT-4 Technical Report.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:35.476156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b039b61-c01f-4da3-8b03-8ac02999b1a1 · outbound

This paper cites Brown, Benjamin Mann, Nick Ryder, et al.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Brown, Benjamin Mann, Nick Ryder, et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:49.914111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 601f27a1-edd7-4b43-af36-e51b845734af · outbound

This paper cites Monte-carlo tree search: A new framework for game ai.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Monte-carlo tree search: A new framework for game ai

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:49.565457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0323abe0-5781-4ca2-97ff-aeaec1a44203 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Evaluating Large Language Models Trained on Code

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:35.677409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afc40f9b-d77e-441d-933c-b521c45a07d6 · outbound

This paper cites Masked thought: Simply masking partial reasoning steps can improve mathematical reasoning learning of language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Masked thought: Simply masking partial reasoning steps can improve mathematical reasoning learning of language models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:49.171065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07121e0d-c9f4-46df-a630-023d2b0f27cf · outbound

This paper cites Alphamath almost zero: process supervision without process.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Alphamath almost zero: process supervision without process

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.961123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 009c5f40-3393-4866-a1e2-21bf0a9a1112 · outbound

This paper cites Autoprm: Automating procedural supervision for multi-step reasoning via controllable question decomposition.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Autoprm: Automating procedural supervision for multi-step reasoning via controllable question decomposition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.814987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2df77f1-ceab-4edd-bc6b-8fb1c7dcaf27 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Training Verifiers to Solve Math Word Problems

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:35.924600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da63f96d-1bc9-4371-b239-287ed4e3dcc9 · outbound

This paper cites To RA : A tool-integrated reasoning agent for mathematical problem solving.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction To RA : A tool-integrated reasoning agent for mathematical problem solving

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.536444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ffbae45-f1eb-4ce6-9a29-de07a09a104b · outbound

This paper cites The llama 3 herd of models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction The llama 3 herd of models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.420140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 070eca6a-c128-4f6b-9c78-5d8f8b75d7dd · outbound

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

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Measuring mathematical problem solving with the math dataset

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.230501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7efde090-51b1-4766-9c6b-3eb6be572110 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Lora: Low-rank adaptation of large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:48.052315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54e8a3a8-2158-478f-81e4-c12985126e62 · outbound

This paper cites Enhancing sequential recommendation via llm-based semantic embedding learning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Enhancing sequential recommendation via llm-based semantic embedding learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.919800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b7c2427-3649-4843-b01d-497ce5223050 · outbound

This paper cites Qdmr-based planning-and-solving prompting for complex reasoning tasks.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Qdmr-based planning-and-solving prompting for complex reasoning tasks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.788424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1791d949-1c1a-416f-afe3-0b55b071eaf1 · outbound

This paper cites OpenAI o1 System Card.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction OpenAI o1 System Card

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:37.012575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc971a1-a164-4508-ad90-fdfb8464211c · outbound

This paper cites Mistral 7B.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mistral 7B

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:37.105940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a2ebb96-0d93-4d22-8458-574b5f59a039 · outbound

This paper cites Thinking, fast and slow.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Thinking, fast and slow

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.655924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f68f9112-af26-4c3c-b68b-52fc9d641eaa · outbound

This paper cites Large language models are zero-shot reasoners.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Large language models are zero-shot reasoners

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.475641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40570e39-9a18-4fd5-8e1f-4bf79b820114 · outbound

This paper cites Mugglemath: Assessing the impact of query and response augmentation on math reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mugglemath: Assessing the impact of query and response augmentation on math reasoning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.306422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30ae21a9-3697-4682-8d0c-057b6567e696 · outbound

This paper cites Neuro-symbolic data generation for math reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Neuro-symbolic data generation for math reasoning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:47.154005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bfcd9395-c9d4-4652-960e-9f735d8a037e · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction The flan collection: Designing data and methods for effective instruction tuning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.996956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15832068-d0e0-4752-a424-19f814cb73ce · outbound

This paper cites Decoupled Weight Decay Regularization.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Decoupled Weight Decay Regularization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:38.024122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d492303-f7db-4746-9a8d-54770475d064 · outbound

This paper cites Mathgenie: Generating synthetic data with question back-translation for enhancing mathematical reasoning of llms.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mathgenie: Generating synthetic data with question back-translation for enhancing mathematical reasoning of llms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.842165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1dd76814-7e06-4411-a34f-cf2b62097d96 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:38.287356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afaf75ee-f5ad-4a49-8e09-9bd7e156b46b · outbound

This paper cites Teaching small language models to reason.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Teaching small language models to reason

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.671866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7703168d-cbb1-4461-9c5c-31021e9110ee · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Direct preference optimization: Your language model is secretly a reward model

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.514009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6acb3fb-cbcd-435a-a1a0-16e0ffc20624 · outbound

This paper cites Self-refine instruction-tuning for aligning reasoning in language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Self-refine instruction-tuning for aligning reasoning in language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.399378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c23022c6-1050-4d60-9094-b4b8585e8e97 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:38.717869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62dbf80c-ad67-481e-a897-8acb0f994243 · outbound

This paper cites Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:38.869952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b14d7a6a-8858-4d4c-a96c-cedda2d535a7 · outbound

This paper cites Mathscale: Scaling instruction tuning for mathematical reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mathscale: Scaling instruction tuning for mathematical reasoning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.240544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e6eb7d5-ef9b-4d0a-94ff-cb7efd6ad040 · outbound

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

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Gemini: A Family of Highly Capable Multimodal Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:39.047248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 947debdb-8abc-4eee-a26a-3c6edcdf5713 · outbound

This paper cites Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:46.036633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9914698-1180-4220-9285-82a4c8f11a90 · outbound

This paper cites Openmathinstruct-1: A 1.8 million math instruction tuning dataset.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Openmathinstruct-1: A 1.8 million math instruction tuning dataset

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:45.778257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e2bd8d7-8bba-4ad7-a063-511c4e1423ad · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:39.328451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40656dcd-6414-4919-8093-67f99d945298 · outbound

This paper cites Zheng, Han Yu, et al.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Zheng, Han Yu, et al

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:45.541953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8840f575-0ec4-4dba-99d5-58fbfcb0d34f · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Self-consistency improves chain of thought reasoning in language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:45.255948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02e2c66f-02cd-4fba-81f3-b84f1c720a14 · outbound

This paper cites Mathcoder: Seamless code integration in LLM s for enhanced mathematical reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mathcoder: Seamless code integration in LLM s for enhanced mathematical reasoning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:44.990681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c08558dd-3ab8-4412-b8be-9bd3c21be0b2 · outbound

This paper cites Math-shepherd: Verify and reinforce llms step-by-step without human annotations.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Math-shepherd: Verify and reinforce llms step-by-step without human annotations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:44.698336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ad24893-ac71-4202-bf7e-e8bcee9b469a · outbound

This paper cites Self-training with direct preference optimization improves chain-of-thought reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Self-training with direct preference optimization improves chain-of-thought reasoning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:44.377065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd7e574d-31dc-4fe7-aff6-0a0eb069b99b · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Chain-of-thought prompting elicits reasoning in large language models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:44.088977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 869f287a-9792-4555-a9c9-bce3104c9463 · outbound

This paper cites From language modeling to instruction following: Understanding the behavior shift in LLM s after instruction tuning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction From language modeling to instruction following: Understanding the behavior shift in LLM s after instruction tuning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:43.807661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec8072ad-46a0-46e5-a24e-aceea15bb982 · outbound

This paper cites Training large language models for reasoning through reverse curriculum reinforcement learning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Training large language models for reasoning through reverse curriculum reinforcement learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:43.521265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f726341-6ce4-4b9a-a0ed-9e8b9234997e · outbound

This paper cites Qwen2.5 Technical Report.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Qwen2.5 Technical Report

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:40.186526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 623361de-c434-4348-a492-5f181dcd6936 · outbound

This paper cites MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-05T19:12:41.504027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54a823a3-aa12-4805-8dad-d92cd6c24d2c · outbound

This paper cites M u M ath: Multi-perspective data augmentation for mathematical reasoning in large language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction M u M ath: Multi-perspective data augmentation for mathematical reasoning in large language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:43.263513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 513c8f9e-06e2-47c6-8c19-b85b6074e673 · outbound

This paper cites OVM , outcome-supervised value models for planning in mathematical reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction OVM , outcome-supervised value models for planning in mathematical reasoning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:42.991624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4806127c-c06c-4e9d-a165-c0474f2a07eb · outbound

This paper cites Metamath: Bootstrap your own mathematical questions for large language models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Metamath: Bootstrap your own mathematical questions for large language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:42.678813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0903efdf-b826-4e9c-97b2-3f80f99a216d · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:40.626109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 788df012-0f81-48c8-ad48-5768b6503222 · outbound

This paper cites Mammoth: Building math generalist models through hybrid instruction tuning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Mammoth: Building math generalist models through hybrid instruction tuning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:42.436343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a78a6695-8d00-409a-b27a-13042f14688d · outbound

This paper cites Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:40.852662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29c6b981-176d-4701-b86e-28e39d865b66 · outbound

This paper cites Learn beyond the answer: Training language models with reflection for mathematical reasoning.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Learn beyond the answer: Training language models with reflection for mathematical reasoning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:42.140233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aee7c0ab-dc84-4e5f-be8f-5800c9752f05 · outbound

This paper cites Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:12:41.888699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 480c79a1-44eb-46de-8375-4e085079bdfe · outbound

This paper cites write newline.

Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction write newline

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T19:12:41.248882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers