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Paper Citation Record · LEDGER

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.15736.

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

pith.paper-citation-record.v1
2607.15736 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:30:30.736777Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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Outbound references

Observation 804d9cb0-e422-4b48-9a42-df52fb8bc6b0 · outbound

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

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning On-policy distillation of language models: Learning from self-generated mistakes

Reference 1

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source=pdf_text observed=2026-08-01T22:30:26.312230Z digest=sha256:5cddee12dc7fc5d773d9bb79d867b9e28eee62ef3af83a963973fd544f18b6f6

Observation 4ca6bead-6994-420f-a141-59d3a0c82426 · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-01T22:30:26.382580Z digest=sha256:3413ff4194c18b6c5a350f833c417414c8b12fcfa63fd4ae05393962d666b797

Observation 71580838-4a00-4043-b714-62b35e844fac · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Evaluating Large Language Models Trained on Code

Reference 3

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source=pdf_text observed=2026-08-01T22:30:26.437579Z digest=sha256:0b4a05e89e5ff5f63a9e4ca16e3fc7a7d90e2cf4e7447a0031b49c44b25b550a

Observation b2d756ed-7aef-46f3-a18d-08c8796c2180 · outbound

This paper cites Over-reasoning and redundant calculation of large language models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Over-reasoning and redundant calculation of large language models

Reference 4

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source=pdf_text observed=2026-08-01T22:30:26.574381Z digest=sha256:927f180ef3eb888cf4f63078c146312154607b16476b2bc3ff0ddddacd51b256

Observation 4aec21af-58bb-4c93-8c00-71d71bec22cb · outbound

This paper cites Ctrlcot: Dual-granularity chain-of-thought compression for controllable reasoning.arXiv preprint arXiv:2601.20467, 2026.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Ctrlcot: Dual-granularity chain-of-thought compression for controllable reasoning.arXiv preprint arXiv:2601.20467, 2026

Reference 5

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source=pdf_text observed=2026-08-01T22:30:26.709576Z digest=sha256:9d57bf5c2bf05ab9c9ff9fd4cfe8ac364315c2dd44334414b6e338faf8592a44

Observation 23771348-14da-43d3-85e7-1bf00c307d4e · outbound

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

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

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source=pdf_text observed=2026-08-01T22:30:26.806144Z digest=sha256:a320f23a1b3954639037da23322a407463636b73bd17423a5236b7ff5ef27f47

Observation cc4ec9f7-12a1-45a4-9c29-d8702dac0d2d · outbound

This paper cites FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

Reference 7

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source=pdf_text observed=2026-08-01T22:30:26.949438Z digest=sha256:beb2401d7f29fc667e68c9cef4f2fca03fe3c654010b64911a5ea5a5f25a78c6

Observation 0584c0fa-fd79-4637-b482-b9222a1d19ad · outbound

This paper cites Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022

Reference 8

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source=pdf_text observed=2026-08-01T22:30:27.059754Z digest=sha256:5e87d06e56cf0e9182c6ef215b38b6fecb7dee38e64a39ba5f8959ed864426c3

Observation 71436f62-523a-4f94-b8c9-41869fc84566 · outbound

This paper cites CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning

Reference 9

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source=pdf_text observed=2026-08-01T22:30:27.185941Z digest=sha256:1498e926260ba33859b31ddd350d9ad7abcbfcc865c96b081fd1afe1628bc755

Observation ce4b9a58-09c5-4fde-a45f-392571590ab6 · outbound

This paper cites Stepwise penalization for length-efficient chain-of-thought reasoning.arXiv preprint arXiv:2603.00296, 2026.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Stepwise penalization for length-efficient chain-of-thought reasoning.arXiv preprint arXiv:2603.00296, 2026

Reference 10

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source=pdf_text observed=2026-08-01T22:30:27.342651Z digest=sha256:78ee12a7740e7348256b5ca9bbf9466848b6379e07e3ed1aa122a3ed5fbe1095

Observation 5b7fbd9e-57fc-47ec-bd72-7c61dbc45b65 · outbound

This paper cites Leash: Adaptive length penalty and reward shaping for efficient large reasoning model.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Leash: Adaptive length penalty and reward shaping for efficient large reasoning model

Reference 11

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source=pdf_text observed=2026-08-01T22:30:27.465715Z digest=sha256:194490e235e9bc09078c26eebf2222c8091416ce71f6b17d52e4ced8f8465af9

Observation 5ba74e4f-fc37-4b55-8f29-fcc8a19eb7ab · outbound

This paper cites Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation

Reference 12

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source=pdf_text observed=2026-08-01T22:30:27.604900Z digest=sha256:db09b8b86cc95dd0ae4f07e7b290d2826ea33c362feba6a5569cf0a2aee101ad

Observation 9ee0426e-f059-4a07-9eb6-34c0452fb5c5 · outbound

This paper cites Making slow thinking faster: Compressing llm chain-of-thought via step entropy.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Making slow thinking faster: Compressing llm chain-of-thought via step entropy

Reference 13

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source=pdf_text observed=2026-08-01T22:30:27.734791Z digest=sha256:8e9b2726cda5fa3a10fec345ee19515c2e78d1f11ab22c2d5245246cf5e4f2f6

Observation a7e33486-b871-43fb-80cd-52bcf714811c · outbound

This paper cites Cot-valve: Length- compressible chain-of-thought tuning.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Cot-valve: Length- compressible chain-of-thought tuning

Reference 14

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source=pdf_text observed=2026-08-01T22:30:27.867246Z digest=sha256:540c123401fa1e8fc1f54548135488262cb87a90b128f81730ee882067061bbf

Observation 0a0d4f9a-1cc5-4e56-9b8c-dcc1d45a5c81 · outbound

This paper cites Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt

Reference 15

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source=pdf_text observed=2026-08-01T22:30:27.977843Z digest=sha256:978d61ed889d11704f8f4fb5100d429693982e60d47f61d9043ae635847e4919

Observation afa4a953-3909-43b0-aa0b-fafb28be5eb1 · outbound

This paper cites The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models

Reference 16

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source=pdf_text observed=2026-08-01T22:30:28.075136Z digest=sha256:cee0156609afedba6a94a46040339d4beb74aed4395fffc8fb4dbe0e2a02bb39

Observation fe6823c0-5e7f-4cbe-82eb-c29ce392c66e · outbound

This paper cites CRISP: Compressed Reasoning via Iterative Self-Policy Distillation.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning CRISP: Compressed Reasoning via Iterative Self-Policy Distillation

Reference 17

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source=pdf_text observed=2026-08-01T22:30:28.143346Z digest=sha256:52d19e4600258468e18b17b2fa57350dadc72bf88939956532c221fe3ae2d118

Observation 5b00ecb9-7a1c-44af-8f27-118e5554b71f · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 18

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source=pdf_text observed=2026-08-01T22:30:28.188121Z digest=sha256:507f4835fa0408957429c95050ccf7ba1e3cc12330ff6ae2a16c884fc8ef125e

Observation 270477ff-04a5-465b-a9fb-d2544c49303c · outbound

This paper cites Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

Reference 19

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source=pdf_text observed=2026-08-01T22:30:28.272756Z digest=sha256:799958cbd3aadd2e57e12025b7ec221fe1950f5c784ffc9846ad44585e8f9724

Observation 38483ab5-3502-41d5-8e6b-e19e2bfebca4 · outbound

This paper cites SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

Reference 20

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source=pdf_text observed=2026-08-01T22:30:28.401425Z digest=sha256:6ba5efe100644bae332b76bb2abbe0a8a7c0b7bbb59f95e98f7750e5c4b27166

Observation 82c46945-f09b-4956-9e47-cd4997d2cdd5 · outbound

This paper cites Mitigating overthinking in large reasoning models via difficulty-aware reinforcement learning.arXiv preprint arXiv:2601.21418, 2026.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Mitigating overthinking in large reasoning models via difficulty-aware reinforcement learning.arXiv preprint arXiv:2601.21418, 2026

Reference 21

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source=pdf_text observed=2026-08-01T22:30:28.521507Z digest=sha256:fa635acd61e45516bb5bd583400e9201198777df3da3bee15509fd01f9b9a6a1

Observation 78dd7ee1-1831-4003-8622-8a91d09a4dcc · outbound

This paper cites Pointrft: Explicit reinforcement fine-tuning for point cloud few-shot learning.arXiv preprint arXiv:2603.23957, 2026.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Pointrft: Explicit reinforcement fine-tuning for point cloud few-shot learning.arXiv preprint arXiv:2603.23957, 2026

Reference 22

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source=pdf_text observed=2026-08-01T22:30:28.646872Z digest=sha256:a77fa315f34013048beb193844c8fcfff7bc09ce1aa3db0fb786ceb3faa30351

Observation 87e7945c-1293-4074-a41a-cd85eacc6e7c · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 23

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source=pdf_text observed=2026-08-01T22:30:28.751256Z digest=sha256:399e152b8fbc05a0325d66dd5e53c4582bcb73da9aa2a42b39f8b10b1ae7b345

Observation 2cfc9306-1f12-4bd2-8e43-202a8f59d53e · outbound

This paper cites Stop spinning wheels: Mitigating llm overthinking via mining patterns for early reasoning exit.arXiv preprint arXiv:2508.17627, 2025.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Stop spinning wheels: Mitigating llm overthinking via mining patterns for early reasoning exit.arXiv preprint arXiv:2508.17627, 2025

Reference 24

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source=pdf_text observed=2026-08-01T22:30:28.856639Z digest=sha256:132929abdfeec535ee07c8b883e04b6c38451a15f3a5f283d5fae620f9eebff0

Observation 8d425e2d-bf39-4158-b6a5-3991ea88868d · outbound

This paper cites Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Reference 25

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source=pdf_text observed=2026-08-01T22:30:29.031776Z digest=sha256:851e89e64b44e0f1a298836105133e6208625454e8abcc5fea137cec457a8630

Observation 3d6ea4e8-f89d-47ae-8438-c12168969145 · outbound

This paper cites Tokenskip: Controllable chain-of- thought compression in llms.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Tokenskip: Controllable chain-of- thought compression in llms

Reference 26

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source=pdf_text observed=2026-08-01T22:30:29.190178Z digest=sha256:5c8729162d1e9d677aecb7495b702fe3b909289d937fd0106b38c7e98b5c1709

Observation 5c24fd4d-33e7-4894-9235-6b49b96cef7d · outbound

This paper cites TIP: Token Importance in On-Policy Distillation.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning TIP: Token Importance in On-Policy Distillation

Reference 27

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source=pdf_text observed=2026-08-01T22:30:29.298845Z digest=sha256:64803d3e917bc463137a08dc2529c4761e09d52f33b10dbb0dc52db65e4fe5f7

Observation 724d3a4e-c076-4d70-ac1f-017cd7a5a2e8 · outbound

This paper cites Qwen3 Technical Report.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Qwen3 Technical Report

Reference 28

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source=pdf_text observed=2026-08-01T22:30:29.435203Z digest=sha256:ba5de995531ade8d12456243df3cef032eec5341dc7f74ba719a0507166f004c

Observation 42a0bf48-4449-4bce-af68-bd9b3541a50f · outbound

This paper cites Dynamic early exit in reasoning models.arXiv preprint arXiv:2504.15895, 2025.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Dynamic early exit in reasoning models.arXiv preprint arXiv:2504.15895, 2025

Reference 29

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source=pdf_text observed=2026-08-01T22:30:29.581049Z digest=sha256:84115f2d845c6be9d8e5ceda4ebef23d66753330fa67007818ca4ea0102e3afc

Observation df229ce0-1db5-4f8c-80cc-8ecb0b81639e · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222–113244, 2026.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222–113244, 2026

Reference 30

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Observation 05a0f91a-5b99-4951-8846-aec3d412fd80 · outbound

This paper cites Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning

Reference 31

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source=pdf_text observed=2026-08-01T22:30:29.855076Z digest=sha256:77d1f44a0c13eb6680d4a249f3aef7fee4223815eb95b48a4d6a68d8964fe715

Observation 5d6f8778-4ca5-491c-af35-36fc05297cde · outbound

This paper cites Not all queries need deep thought: Coficot for adaptive coarse-to-fine stateful refinement.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Not all queries need deep thought: Coficot for adaptive coarse-to-fine stateful refinement

Reference 32

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source=pdf_text observed=2026-08-01T22:30:29.983687Z digest=sha256:c68852e9c6b75d35c4263756b7403fcad76da74eb43a2001f259a7e0cd520336

Observation 963e6cda-bb0b-4ced-a1b5-ba1ac8760988 · outbound

This paper cites Pointcot: A multi-modal benchmark for explicit 3d geometric reasoning.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Pointcot: A multi-modal benchmark for explicit 3d geometric reasoning

Reference 33

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source=pdf_text observed=2026-08-01T22:30:30.130283Z digest=sha256:da6d13e8154fd0afd43f8f60f53ca7f6a1c2f2a8d89e8e46c84a7ffe9448ea65

Observation c450edfb-9a75-4e2c-aadd-849da054a64c · outbound

This paper cites Chain-of- thought compression should not be blind: V-skip for efficient multimodal reasoning via dual-path anchoring.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Chain-of- thought compression should not be blind: V-skip for efficient multimodal reasoning via dual-path anchoring

Reference 34

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source=pdf_text observed=2026-08-01T22:30:30.246127Z digest=sha256:f2cda079ad41d17bbb795b0e8ef00515d26e28c29218325114e2254ddd24d9d6

Observation aa0c6422-aac5-40c6-ad1c-39a49fad9520 · outbound

This paper cites find the time 1000 days later.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning find the time 1000 days later

Reference 35

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source=pdf_text observed=2026-08-01T22:30:30.383699Z digest=sha256:a0d0ba1a859893f994c31c70cd33a2f0ca4b3b3c959d27255f23d46ea54be892

Observation 55b36b39-b8ab-4adb-aa77-48b40343be77 · outbound

This paper cites OPSDL: On-Policy Self-Distillation for Long-Context Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning OPSDL: On-Policy Self-Distillation for Long-Context Language Models

Reference 36

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source=pdf_text observed=2026-08-01T22:30:30.506956Z digest=sha256:0b9440bbe0c76efab64006bafa54690e0fd760811a8ab5fe55c5cd96680f3133

Observation 1ce0a45e-fefc-4f5a-88a4-1f59748e649e · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 37

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source=pdf_text observed=2026-08-01T22:30:30.618642Z digest=sha256:54cad771df0f7a09b084bdf5186672095fd004d7b41e8ce2806c4ba62495ce27

Observation aae79c50-4483-4cf4-ba8b-7467a79da262 · outbound

This paper cites ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains.

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains

Reference 38

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