Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T19:12:41.248882Z
Paper Citation Record · LEDGER
Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2508.13037.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
53 of 53 outbound references displayed
- verified exact1
- verified fuzzy37
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.
Observation 480c79a1-44eb-46de-8375-4e085079bdfe · outbound
This paper cites 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.
Pith citing papers
No inbound Pith citation observations are available.