Pith. sign in

Paper Citation Record · LEDGER

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 3 inbound Pith citation observations for arXiv:2607.13124.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.13124 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:13:56.042393Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T04:21:09.717262Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved44
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05495e38-88c2-409c-813c-cf74dbfa49ec · outbound

This paper cites The Llama 3 Herd of Models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation The Llama 3 Herd of Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:50.733093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:50.733093Z digest=sha256:a81ae30e7a31e17589d27759d34233e364937d0987fa2722dc6578ff88171375

Observation 59a18c68-ba1d-424c-ac6d-e2e404c2f5f7 · outbound

This paper cites Qwen3 Technical Report.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Qwen3 Technical Report

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:50.834020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:50.834020Z digest=sha256:db511c19aaf96d1bcc8c8ac0e7bf2425bc63918a2a9b344daa9e9cc9897b9915

Observation bac50476-99a8-40d7-89f2-d194284c570c · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Llm-pruner: On the structural pruning of large language models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:50.939626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:50.939626Z digest=sha256:011c02ecd8ef8ca12cb93dd1502d85e6d434c6c13f8b95ccfed5d01d7e33b52d

Observation b2801b24-01d1-4d08-9a74-b0cdcfe29383 · outbound

This paper cites Slicegpt: Compress large language models by deleting rows and columns.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Slicegpt: Compress large language models by deleting rows and columns

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.058227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.058227Z digest=sha256:321fb8c7dceee7a79d319fa53aaae449607a2875febe6fb978f1fd29d28be02a

Observation 98a75319-c8e5-4d1d-9272-a1cab40e5f0a · outbound

This paper cites Shortgpt: Layers in large language models are more redundant than you expect.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Shortgpt: Layers in large language models are more redundant than you expect

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.132532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.132532Z digest=sha256:847c03e66b1c237b31887dedaee06c5ad5d73886b23673d37baa6d004b532de4

Observation 447b6d32-80d7-4faf-b909-c9019dd736cf · outbound

This paper cites Sheared llama: Accelerating language model pre-training via structured pruning.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Sheared llama: Accelerating language model pre-training via structured pruning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.220978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.220978Z digest=sha256:31cd053e5f93747bae2704eebc9d279285b57b98a45358806d8022def576d06d

Observation b65ef1c2-dbac-4242-8ede-ca3324d1dac7 · outbound

This paper cites Compact language models via pruning and knowledge distillation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Compact language models via pruning and knowledge distillation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.290260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.290260Z digest=sha256:c816b631a99d65563578ddf6d4597bad3ea605df6df96030e088d6da19570c2d

Observation 18d4b32c-1a95-4b5d-a7ed-1b7a59dfe25d · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.430620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.430620Z digest=sha256:99d5f0b21ef19e37a600d18705f63e2f36a4ea95e9542bf0b51b5f5516d83bcb

Observation 092b3969-111e-4379-833b-01624dcfca21 · outbound

This paper cites A simple and effective pruning approach for large language models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation A simple and effective pruning approach for large language models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.480116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.480116Z digest=sha256:b6c6eab95218c1bed9e870ab7b20a746348345f0205957edd4adff60a791af51

Observation 61d1f87c-c33c-4436-b10a-27bb40d92fcf · outbound

This paper cites Awq: Activation-aware weight quantization for llm compression and acceler- ation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Awq: Activation-aware weight quantization for llm compression and acceler- ation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.546450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.546450Z digest=sha256:232b879f3d8beb701b2541a0559bcc6bda7455936e2390488efea5213916dbec

Observation 9ca4910d-192a-464a-aa0b-a2d7a2b45f2e · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Fluctuation-based adaptive structured pruning for large language models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.648459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.648459Z digest=sha256:e36c50b5169bb8de10af22c04626ca0ee5f091af1bd08c777f57c94c85114de5

Observation 90b85378-7099-4928-9953-33e05e129f27 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.714816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.714816Z digest=sha256:0315dcfe0d4dfa3f1c1cf9dfc19d0a518905d415400afc1f4479b46e67a57ec8

Observation e203ea91-0583-4939-a257-1781b16f1d00 · outbound

This paper cites The unreasonable ineffectiveness of the deeper layers.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation The unreasonable ineffectiveness of the deeper layers

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.786642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.786642Z digest=sha256:6ac7d8ec4051d320822e8ead7c11d28c2651cfca607c21a135a84e142f3781dc

Observation f5951686-c711-4db2-bed7-9eaaf68a9664 · outbound

This paper cites Measuring massive multitask language understanding.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Measuring massive multitask language understanding

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.856938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.856938Z digest=sha256:a97ae8c2d17e8ab967c2eaba19f3cd917536b0a9852560567676faa50778b97f

Observation fc40ada5-e662-4a22-b5b9-587583dbe7ab · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InAnnual Meeting of the Association for Computational Linguistics (ACL), 2019.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Hellaswag: Can a machine really finish your sentence? InAnnual Meeting of the Association for Computational Linguistics (ACL), 2019

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:51.944481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:51.944481Z digest=sha256:35fc748db5c99b14b6df2895d7f124cec558552f924ab0898a503b25abb85e1a

Observation a00f185f-1008-4ab3-89cd-12e219de46ee · outbound

This paper cites The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.012484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.012484Z digest=sha256:00199d24b5eeb99001630f713f9e4580caa540f15199f0d7ed4c540c5c7315d5

Observation af178499-47e5-4b3d-a294-ce4a1197e2b3 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Evaluating Large Language Models Trained on Code

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.078035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.078035Z digest=sha256:7d30e73c8319f367cef4376cac377d6eb68f64ba378ec48cc32c10db96f37056

Observation b2de341b-7f6f-458a-9d89-f4783912ea1c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Training Verifiers to Solve Math Word Problems

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.185791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.185791Z digest=sha256:60b383165170b4abe608895b518b2b62569ef40e80cb4823b3d9f20e4fef33b9

Observation f92c7253-6d76-4f44-9c07-fdd2061fdd7d · outbound

This paper cites Sequence level training with recur- rent neural networks.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Sequence level training with recur- rent neural networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.395465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.395465Z digest=sha256:4c51a71559f21727d5a4412de1c62deaff26ab63d01f61714b03a71dd4a4bea2

Observation 04bb9010-73a1-4e7d-bc22-ef70ddc337e8 · outbound

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

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation On-policy distillation of language models: Learning from self-generated mistakes

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.549293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.549293Z digest=sha256:78ca649cf76ea676ca38c0a7814fb6e28fa779a9a591e01cf411b611c38bdb14

Observation dabbd62a-fe92-4b96-a798-9531be65ca9e · outbound

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

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.717632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.717632Z digest=sha256:758c5ebe1dff4fa7a46ef5dc51e8180784f4166efb2c4045fe9580dd1fffb9b0

Observation 3160d23c-ec95-4040-a91c-803f0b925421 · outbound

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

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:52.935572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:52.935572Z digest=sha256:02a3a41ad90d6dc3a514cc946d9d7501b62f095ef2728f51129db957df2d3b3d

Observation 5313e3eb-834f-42db-b8cf-601a754cafa3 · outbound

This paper cites Sequence-level knowledge distillation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Sequence-level knowledge distillation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.093884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.093884Z digest=sha256:eba117fe44777069270db5c7858ffb0a358f97657142f8fab7b88d35b8896003

Observation 9ed40101-740c-4462-900d-3ebc0969e68c · outbound

This paper cites Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.238015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.238015Z digest=sha256:25c14429389f67478fd54ece90ddade8b9d5166a28735d73cb24074dac88c20d

Observation 78aa3d74-6feb-44d6-936a-ff738338db62 · outbound

This paper cites The curious case of neural text degeneration.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation The curious case of neural text degeneration

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.357398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.357398Z digest=sha256:ebf290c8917d92e81692ed93fb7c6e36710ea6a4e5c301575e2c038a05e6c84a

Observation 0f00dc62-5eb6-49bb-8edf-122584093759 · outbound

This paper cites Learning to break the loop: Analyzing and mitigating repetitions for neural text generation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Learning to break the loop: Analyzing and mitigating repetitions for neural text generation

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.538637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.538637Z digest=sha256:452af3471764fcf660b5d2828f2f5a13faa0a663635d11443a3f4042e5ae37c3

Observation f9798946-738c-48ab-9ab9-21b4c24f6905 · outbound

This paper cites Laco: Large language model pruning via layer collapse.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Laco: Large language model pruning via layer collapse

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.626628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.626628Z digest=sha256:0b0ea80532db4d8694687c3d14b557f2ca376f32d16b8f596a68082c86a06f1c

Observation 3eaf089d-7a60-42da-953b-1dfa8d99b9f1 · outbound

This paper cites Shortv: Efficient multimodal large language models by freezing visual tokens in ineffective layers.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Shortv: Efficient multimodal large language models by freezing visual tokens in ineffective layers

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.811763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.811763Z digest=sha256:12947330ee11e21190f837a063331b24f5da7221cc11bcb8bb2f40aa03080c88

Observation 2b60f8fe-4039-475e-8415-daccc91cbe80 · outbound

This paper cites Everybody prune now: Structured pruning of llms with only forward passes.arXiv preprint arXiv:2402.05406, 2024.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Everybody prune now: Structured pruning of llms with only forward passes.arXiv preprint arXiv:2402.05406, 2024

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:53.939812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:53.939812Z digest=sha256:5e6a7c0be7abc26409c29745025b39f9dc8bebd9471341cea892b9ab10a703c7

Observation c04183cb-d4b0-440b-9d57-4a61ba1b9793 · outbound

This paper cites LLM Pruning and Distillation in Practice: The Minitron Approach.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation LLM Pruning and Distillation in Practice: The Minitron Approach

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.023861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.023861Z digest=sha256:3ee12cc555ba61feec49478121b950bee44435ee3d3a2a034f62bfdb903c0b75

Observation 692bf304-c130-4733-a940-4c62977bc173 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Distilling the Knowledge in a Neural Network

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.112330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.112330Z digest=sha256:56f9433df587c238ef4ddbb8f93b2c8dd374385976e62f542bc8c5a7676d9502

Observation 063a0a2e-7776-43fb-8c5a-d3186026af8f · outbound

This paper cites Autoregressive knowledge distillation through imitation learning.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Autoregressive knowledge distillation through imitation learning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.291104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.291104Z digest=sha256:bbd8d5150b9b21f467d1f79fc25f8c7b60d1b41e910a67bb5db24e6b0b9fbb63

Observation 674bed9f-6553-4c2a-bd84-87c1c20744fb · outbound

This paper cites Minillm: On-policy distillation of large language models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Minillm: On-policy distillation of large language models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.498708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.498708Z digest=sha256:f67188ee19e6536eb52bc3397c13bafed36d5f4232815eb44a7281af383df40e

Observation ce47cacf-02d3-48b6-8bdb-df892ea4b61b · outbound

This paper cites f-divergence minimization for sequence-level knowledge distillation.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation f-divergence minimization for sequence-level knowledge distillation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.607136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.607136Z digest=sha256:dff4c0d40ef8fe2df984d6365cfea1c84f45d595d815686be741918a9dc8a7c3

Observation 4cf03823-4f2a-4e9b-8d67-46f5e0252d6d · outbound

This paper cites Distillm: Towards streamlined distillation for large language models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Distillm: Towards streamlined distillation for large language models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.686739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.686739Z digest=sha256:ae8ada408cdb61a48573554c2e4aa5b2267ab802076b36b45cee30fa37d97d44

Observation 51638354-1de4-4c03-bc45-513f77a2f90c · outbound

This paper cites Tulu 3: Pushing frontiers in open language model post-training.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Tulu 3: Pushing frontiers in open language model post-training

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.766889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.766889Z digest=sha256:f9d7aefaa26e537876b5fccd4b33c0e8a781fb4524ebb58594e6f8fe8876e4f0

Observation 708f36d5-34e8-43a9-9dde-760c942c83f9 · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Dapo: An open-source llm reinforcement learning system at scale

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:54.856534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:54.856534Z digest=sha256:7ea290a027432827dbccf16dbb11cea957c8c13a8b99643cc24ee6508636b894

Observation c7f38387-6fa5-4d06-8407-10b71816342c · outbound

This paper cites Proximal Policy Optimization Algorithms.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Proximal Policy Optimization Algorithms

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.039137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.039137Z digest=sha256:48c607502b9504220ef96db400f17de20ed78757c82239e22e0e982c81d94877

Observation 9bb3807e-8072-4dfd-9f64-8a0cdeb1434c · outbound

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

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Measuring mathematical problem solving with the math dataset

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.238285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.238285Z digest=sha256:7aeda66c4d205ee242112df17fea0cd6f68e1cca377def96ad382eeea77ec0d1

Observation b774555d-5d2e-45de-9f48-4967f1691e10 · outbound

This paper cites Opencodeinstruct.https://huggingface.co/datasets/nvidia/OpenCodeInstruct, 2024.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Opencodeinstruct.https://huggingface.co/datasets/nvidia/OpenCodeInstruct, 2024

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.418696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.418696Z digest=sha256:dfe5928b54a66015af9043a3d7c3dbb0d0cc7fb488d550f1ce60d735240f11bb

Observation 230d03e4-7dc9-450f-bb56-eccd7e75b4b9 · outbound

This paper cites Program Synthesis with Large Language Models.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Program Synthesis with Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.557330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.557330Z digest=sha256:30de0868850866948d995dc3cd016f6eac65ac57d2415177eff2b40c3b70a724

Observation 5d57a98e-3cb1-442d-96d1-d2c17c8ebf9d · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality.https://lmsys.org/blog/2023-03-30-vicuna/, 2023.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality.https://lmsys.org/blog/2023-03-30-vicuna/, 2023

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.708035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.708035Z digest=sha256:ac20c92739c4fbf1567eb0b0e5bb59a8af7b487a86c959c0969ea3ce4f3991c6

Observation 7b2fd1c5-bada-42fd-b5ce-f41c52d4e1e4 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation Enhancing chat language models by scaling high-quality instructional conversations

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:55.904741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:55.904741Z digest=sha256:3f3d1fa2f4fa292e4e478b84fa3dc9b4da62ce712173f7cfdf21c6f072682512

Observation f804196f-7868-49a1-8021-a8980a185687 · outbound

This paper cites ).").").

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation ).").")

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-02T06:13:56.042393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:13:56.042393Z digest=sha256:bef0a3630f71af78de34d032ea64905ae351948e6a979160929f5104c57e7db6

Pith citing papers

Observation 2dfe51cd-26ce-4516-8c0d-effef46be78d · inbound

IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models cites this paper.

IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

Reference 137

Resolution
unresolved
no resolver link, observed 2026-08-04T04:21:09.717262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T04:21:09.717262Z digest=sha256:22154fa9efe423c2850980fdb5a4c8c7337ee1173629ee21435c953e7bc4cf08

Observation 61e61b6b-01f6-4e19-8ba0-0a5ea3526c76 · inbound

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment cites this paper.

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-30T11:44:37.034752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T11:44:37.034752Z digest=sha256:b4dcd13e300e8daa5b4395f7e66dbf6afaf8636e252dcec5d6da45af752c6c49

Observation f7a55579-28d9-413b-b505-3b28b724d7f8 · inbound

Adaptive FastOPD: Progress-Aware Rollout Horizon Expansion for Efficient On-Policy Distillation cites this paper.

Adaptive FastOPD: Progress-Aware Rollout Horizon Expansion for Efficient On-Policy Distillation ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-03T05:56:32.554568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T05:56:32.554568Z digest=sha256:9cc17524d1fe4b5a597521e1f2b3fd86ea0307c51d3005ccab8288456461003b