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Paper Citation Record · LEDGER

Trust Region Policy Distillation

As of 5 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.04751.

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

pith.paper-citation-record.v1
2607.04751 v1

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measured 27 of 27 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-11T14:06:50.522833Z

measured 27 of 27 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

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Reference resolution

27 of 27 outbound references displayed

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Outbound references

Observation 50d9bc73-025b-4908-8b91-bf8db5a8b244 · outbound

This paper cites Deepseek-v4: Towards highly efficient million-token context intelligence, 2026.

Trust Region Policy Distillation Deepseek-v4: Towards highly efficient million-token context intelligence, 2026

Reference 1

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Observation 45a6f1f5-e1ee-4487-81b7-3095a2c82701 · outbound

This paper cites Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes.

Trust Region Policy Distillation Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes

Reference 2

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Observation a3834600-6e66-434f-bcfb-fc6935b88551 · outbound

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

Trust Region Policy Distillation MiniLLM: Knowledge distillation of large language models

Reference 3

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Observation 94f8dc6f-a5d1-4b87-a35b-e4f0dd30cbb1 · outbound

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

Trust Region Policy Distillation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

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Observation 60f34f8a-e0a9-44c5-856c-7a84f9ac2310 · outbound

This paper cites Stable On-Policy Distillation through Adaptive Target Reformulation.

Trust Region Policy Distillation Stable On-Policy Distillation through Adaptive Target Reformulation

Reference 5

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Observation 44d491eb-2600-47b8-b29b-17eb3d9f1767 · outbound

This paper cites Approximately optimal approximate reinforcement learning.

Trust Region Policy Distillation Approximately optimal approximate reinforcement learning

Reference 6

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Observation d6a37485-07be-4e10-8dfc-d26184901118 · outbound

This paper cites Scaling reasoning efficiently via relaxed on-policy distillation.arXiv preprint arXiv:2603.11137, 2026.

Trust Region Policy Distillation Scaling reasoning efficiently via relaxed on-policy distillation.arXiv preprint arXiv:2603.11137, 2026

Reference 7

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Observation b2b9e8c0-55e7-4d50-9258-c60f58956d2b · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Trust Region Policy Distillation Efficient memory management for large language model serving with pagedattention

Reference 8

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Observation f7f7d7cd-9fac-4721-b148-8f9a5b4f4dc4 · outbound

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

Trust Region Policy Distillation Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 9

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Observation 5b251c66-e121-4082-8b9a-823cbd264d83 · outbound

This paper cites On-policy distillation.Thinking Machines Lab: Con- nectionism, 2025.

Trust Region Policy Distillation On-policy distillation.Thinking Machines Lab: Con- nectionism, 2025

Reference 10

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Observation d49b585c-a0c8-45de-9ed6-f5fa94f645d2 · outbound

This paper cites Trust region policy optimization.

Trust Region Policy Distillation Trust region policy optimization

Reference 11

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Observation bf3b2550-5ff7-4784-b776-4a191fdde080 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Trust Region Policy Distillation Proximal Policy Optimization Algorithms

Reference 12

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Observation afd0ed7b-01ba-4327-9cab-59f833d905c2 · outbound

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

Trust Region Policy Distillation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 13

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Observation 63e02167-10e1-4612-9d5c-85f7d2d2ef8f · outbound

This paper cites Self-Distillation Enables Continual Learning.

Trust Region Policy Distillation Self-Distillation Enables Continual Learning

Reference 14

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Observation 65d21487-4e9b-434c-8227-b22a82babf2a · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Trust Region Policy Distillation Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 15

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Observation b153a3be-9986-448e-b601-1415b55cc3f0 · outbound

This paper cites MiMo-V2-Flash Technical Report.

Trust Region Policy Distillation MiMo-V2-Flash Technical Report

Reference 16

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Observation 36fe3aac-f7d1-47ab-885e-6d4e50b1a936 · outbound

This paper cites Simple policy optimization.

Trust Region Policy Distillation Simple policy optimization

Reference 17

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Observation 43792c86-26fd-407b-ad2f-bbd065be67eb · outbound

This paper cites Qwen3 Technical Report.

Trust Region Policy Distillation Qwen3 Technical Report

Reference 18

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Observation 2d2d46c9-a650-4c39-8ad9-1c94a96a044a · outbound

This paper cites Nemotron-cascade 2: Post-training llms with cascade rl and multi-domain on-policy distillation.arXiv preprint arXiv:2603.19220, 2026.

Trust Region Policy Distillation Nemotron-cascade 2: Post-training llms with cascade rl and multi-domain on-policy distillation.arXiv preprint arXiv:2603.19220, 2026

Reference 19

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Observation a83808d3-167f-418c-a116-17a9d7b50b21 · outbound

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

Trust Region Policy Distillation DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 20

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Observation 15a3054d-25f5-4980-90e9-a0396ab1ffb0 · outbound

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

Trust Region Policy Distillation GLM-5: from Vibe Coding to Agentic Engineering

Reference 21

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Observation 8f6a5ad7-ef3a-42bf-b02b-4a0d8abc3c07 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Trust Region Policy Distillation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 22

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Observation ded7804d-05a7-4db6-8cb3-26de4fa5ee7e · outbound

This paper cites Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583, 2024.

Trust Region Policy Distillation Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583, 2024

Reference 23

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Observation fba2cae8-6ce9-487d-9d2b-fadc178e8967 · outbound

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Trust Region Policy Distillation Unresolved cited work

Reference 24

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Observation 5a8cda0c-cbe3-43ea-8c65-be5b750ea4b9 · outbound

This paper cites Since limt→1+ w(t) = 0 , w(t)<0for allt∈(1,2).

Trust Region Policy Distillation Since limt→1+ w(t) = 0 , w(t)<0for allt∈(1,2)

Reference 25

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Observation e00aaba0-1cd5-4ae5-ad0b-449741e1e412 · outbound

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Trust Region Policy Distillation Unresolved cited work

Reference 26

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Observation 50a270dc-2fab-4272-9fc5-41f210f48b29 · outbound

This paper cites Since there is only one critical point, this unique stationary point corresponds to the global maximum of f(u).

Trust Region Policy Distillation Since there is only one critical point, this unique stationary point corresponds to the global maximum of f(u)

Reference 27

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