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Paper Citation Record · LEDGER

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

As of 19 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.09226.

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

pith.paper-citation-record.v1
2608.09226 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:17:26.218371Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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  • verified fuzzy1
  • unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6ef6fb9-7cad-456b-ab7e-3c58bc6b4177 · outbound

This paper cites Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models

Reference 1

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verified exact
local_arxiv, observed 2026-08-11T21:17:28.103221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T21:17:25.884274Z digest=sha256:75ec3baad95d5a01acdabf3293faccfa5baf9e068a5e6d7174f5ac9222072a3e

Observation d7066390-cbaf-47dd-86c0-ced4649d5272 · outbound

This paper cites Directly fine-tuning diffusion models on differentiable rewards.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Directly fine-tuning diffusion models on differentiable rewards

Reference 3

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:17:25.922513Z digest=sha256:9935046f1d8042dc256a7ce0b1c16eb59986161d6bddb99baaadf33c4cdd96c3

Observation 473df447-f9ed-4b85-bac9-148502b865f4 · outbound

This paper cites Distribution Matching Distillation Meets Reinforcement Learning.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Distribution Matching Distillation Meets Reinforcement Learning

Reference 7

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source=pdf_text observed=2026-08-11T21:17:25.986375Z digest=sha256:b7399034c6d6eee183fc78439a9a35aa06f61645e27d6f276023d5b6c5290c24

Observation 2cf23804-0409-40a2-a7ac-130234d1aedd · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Offline Reinforcement Learning with Implicit Q-Learning

Reference 8

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source=pdf_text observed=2026-08-11T21:17:26.006383Z digest=sha256:1927307b0959a0eccad912501dfe1d6652f00f52805886e1f9289bbe92edba0b

Observation 726c62d7-b23a-43ee-a52a-c9ca6d1d4cd1 · outbound

This paper cites Flow Matching for Generative Modeling.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Flow Matching for Generative Modeling

Reference 10

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source=pdf_text observed=2026-08-11T21:17:26.064758Z digest=sha256:a8579337ae74f5dcb60fbf96361ae4bb652b94ad73ab0842158b79726585670b

Observation bb059e99-97e5-4fd3-8d70-a2b1e5072936 · outbound

This paper cites Flow-GRPO: Training Flow Matching Models via Online RL.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Flow-GRPO: Training Flow Matching Models via Online RL

Reference 11

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source=pdf_text observed=2026-08-11T21:17:26.093900Z digest=sha256:b534982d831bc2209f77159ac48ce1106891376da29cb6cf0ad25c8cf0d33f6c

Observation 58454eb9-a254-4c74-9123-1530b1d4479a · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 13

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source=pdf_text observed=2026-08-11T21:17:26.123274Z digest=sha256:88f82e9215ff9978b7d1a417edd40578558d79be256185f64a3e274a545daa9f

Observation 8f9b5254-5703-4054-93e1-300d8d3225a6 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 14

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source=pdf_text observed=2026-08-11T21:17:26.140360Z digest=sha256:5437856e99fea49d81b506d32f6f1a674f38e1ee109b1db7bea9578797c5354f

Observation 12332ecc-eda5-460c-91b0-0c5567f77b29 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Progressive Distillation for Fast Sampling of Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-11T21:17:26.153697Z digest=sha256:6914263fc997fc94f7453f26acc16ca2f4801424275008b97ca78ddf6cc51299

Observation dcac9a15-986f-4b5b-aa93-474bfd6e79f6 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 16

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source=pdf_text observed=2026-08-11T21:17:26.163274Z digest=sha256:150e8d78ddfe71516086ba044d5feb0657bba680504ab0a41671c5f29a4a7517

Observation 7019b0b7-0339-470b-b30c-56e65343f8e4 · outbound

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

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 17

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source=pdf_text observed=2026-08-11T21:17:26.177481Z digest=sha256:365fc9d302e662f7d9f6e40e02ad209d40a6dbd3e45bfbf1e83e876a968d5b84

Observation 22624f64-5525-4fb1-bba9-625fa3e55efe · outbound

This paper cites Denoising Diffusion Implicit Models.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Denoising Diffusion Implicit Models

Reference 18

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source=pdf_text observed=2026-08-11T21:17:26.187733Z digest=sha256:d9744d2424f8bd846bdaa127e9d7df45748b1f43e13b882b8aeb315e524f1d0d

Observation 7ba2f7f8-2b04-4470-8eed-cb7efd88b471 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 19

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source=pdf_text observed=2026-08-11T21:17:26.194183Z digest=sha256:17072bc315ebeabda7d3654d218dbf9173e1b9f8d2a3acdafe1875357554956d

Observation feeea68e-5c33-4454-a6b1-4ba886e1b374 · outbound

This paper cites Advantage weighted matching: Aligning rl with pretraining in diffusion models.arXiv preprint arXiv:2509.25050, 2025a.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Advantage weighted matching: Aligning rl with pretraining in diffusion models.arXiv preprint arXiv:2509.25050, 2025a

Reference 20

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source=pdf_text observed=2026-08-11T21:17:26.211239Z digest=sha256:9ec0cef692b9f800b6c18acc14afd5ae332be36c31aedd794a32e63afc4932e1

Observation 699355d9-77ee-4507-aec4-0762aac548ec · outbound

This paper cites DiffusionNFT: Online Diffusion Reinforcement with Forward Process.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation DiffusionNFT: Online Diffusion Reinforcement with Forward Process

Reference 21

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source=pdf_text observed=2026-08-11T21:17:26.218371Z digest=sha256:703399b4310a8a87e1ce60461bb5c88025aa2ce1ccf0328c5157a3f6646e8378

Observation 5ec2ee1a-cdde-476c-bd51-62f397d79f91 · outbound

This paper cites MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE

Reference 2021

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source=pdf_text observed=2026-08-11T21:17:26.024780Z digest=sha256:dd510a3ed8608086d0bcf9516ac688b15c10d9d850706fe81ab3677ea8c699d4

Observation be0e1a16-fb43-456c-92b7-5598315f6930 · outbound

This paper cites Reinforcing Few-step Generators via Reward-Tilted Distribution Matching.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Reinforcing Few-step Generators via Reward-Tilted Distribution Matching

Reference 2022

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verified exact
local_arxiv, observed 2026-08-11T21:17:27.654762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T21:17:25.974465Z digest=sha256:4e672069c1e00e97c77a1b29591650ac87dc13bae54a764e43191f58a8d1bdb4

Observation cd14edf4-76b2-492d-bce1-b10f91f398f6 · outbound

This paper cites Classifier-Free Diffusion Guidance.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Classifier-Free Diffusion Guidance

Reference 2023

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source=pdf_text observed=2026-08-11T21:17:25.964609Z digest=sha256:759eb0620967ccf3c458f1af9560d388cd0cead75677a41624b689e452dc6b63

Observation 7ededbd3-4a34-4779-850e-d592ca3d1107 · outbound

This paper cites Rdm: Re-conceptualizing distribution matching as a reward for diffusion distillation.arXiv preprint arXiv:2603.28460,.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Rdm: Re-conceptualizing distribution matching as a reward for diffusion distillation.arXiv preprint arXiv:2603.28460,

Reference 2024

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source=pdf_text observed=2026-08-11T21:17:25.951206Z digest=sha256:5f0bb55da30ba33e8775b86cbe92d9ab89c622f126823c6fdaeec2256360d622

Observation 7a4f1880-265e-4a55-9d69-9cd75a243207 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 2025

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source=pdf_text observed=2026-08-11T21:17:26.109193Z digest=sha256:fedbb40fc3324d342ae3a9a9d0918ec3be655fc954986438a082f3546a9f2120

Observation e2a10241-16b8-4513-b328-2d4e8bdb95de · outbound

This paper cites Nft: Bridging supervised learning and reinforcement learning in math reasoning.

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation Nft: Bridging supervised learning and reinforcement learning in math reasoning

Reference 2026

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verified fuzzy
raw_fallback, observed 2026-08-11T21:17:28.329247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T21:17:25.910457Z digest=sha256:d7612c9dffbb3dcaeb91d6b654b2e8c85538754c64ee1e6c1d201642cf9a66ea

Pith citing papers

No inbound Pith citation observations are available.