Paper Citation Record · LEDGER

How Far Are We from Optimal Reasoning Efficiency?

As of 18 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2506.07104.

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

pith.paper-citation-record.v1
2506.07104 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:49:40.577890Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:48.051608Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T13:22:55.270834Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy5
unresolved54
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcf89b3e-d763-4ed3-af92-b11c20922e51 · outbound

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

How Far Are We from Optimal Reasoning Efficiency? L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:34.499159Z

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Observation 8db244d9-d272-41db-af6f-7ea1ce392dfc · outbound

This paper cites Training language models to reason efficiently.arXiv preprint arXiv:2502.04463, 2025.

How Far Are We from Optimal Reasoning Efficiency? Training language models to reason efficiently.arXiv preprint arXiv:2502.04463, 2025

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:34.596778Z

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Observation 6938d49c-3201-49b0-89f0-480e540e7bec · outbound

This paper cites HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs.

How Far Are We from Optimal Reasoning Efficiency? HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:34.696226Z

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Observation 9769068b-d3e3-4743-a4f4-d51336ab5248 · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

How Far Are We from Optimal Reasoning Efficiency? Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.028025Z

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Observation 24297eed-6dbe-462b-9d27-58e0c3675a5e · outbound

This paper cites Thinkless: LLM Learns When to Think.

How Far Are We from Optimal Reasoning Efficiency? Thinkless: LLM Learns When to Think

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.129268Z

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Observation e92c41ce-5ba5-40f2-a2e8-addd6482a401 · outbound

This paper cites Efficiently Scaling LLM Reasoning with Certaindex.

How Far Are We from Optimal Reasoning Efficiency? Efficiently Scaling LLM Reasoning with Certaindex

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.259278Z

Source-reported events for the cited work

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Observation e3fa284a-d61b-4d75-a819-64c533390610 · outbound

This paper cites Reasoning without self-doubt: More efficient chain-of-thought through certainty probing.

How Far Are We from Optimal Reasoning Efficiency? Reasoning without self-doubt: More efficient chain-of-thought through certainty probing

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.358710Z

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Observation c1ffa9ca-0a8a-4e7c-9c76-86b37ecdcb0b · outbound

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

How Far Are We from Optimal Reasoning Efficiency? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.468940Z

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Observation 640a7e38-dd98-47ee-bef0-a35c7b92c105 · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

How Far Are We from Optimal Reasoning Efficiency? Token-Budget-Aware LLM Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.589682Z

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Observation 9e3c7f12-3266-4a4c-a2fd-a26d3488b72c · outbound

This paper cites Skywork open reasoner series.

How Far Are We from Optimal Reasoning Efficiency? Skywork open reasoner series

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:42.383342Z

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 549e5200-69e8-4e89-9a22-a52ccaa7d237 · outbound

This paper cites ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning.

How Far Are We from Optimal Reasoning Efficiency? ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.815835Z

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Observation acdd038c-6254-4ca2-82e8-ba764b5ad559 · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

How Far Are We from Optimal Reasoning Efficiency? Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:35.897085Z

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Observation 96c1265e-c539-45b8-b150-55341afd6eac · outbound

This paper cites Efficient Test-Time Scaling via Self-Calibration.

How Far Are We from Optimal Reasoning Efficiency? Efficient Test-Time Scaling via Self-Calibration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.050461Z

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Observation 7b95b0bf-bfc5-4c36-a823-b52e471b94d0 · outbound

This paper cites Think Only When You Need with Large Hybrid-Reasoning Models.

How Far Are We from Optimal Reasoning Efficiency? Think Only When You Need with Large Hybrid-Reasoning Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.120187Z

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Observation 31a2eb7d-20eb-4a0d-a9e9-925851659732 · outbound

This paper cites C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness.

How Far Are We from Optimal Reasoning Efficiency? C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.187134Z

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Observation d09a825c-5489-4883-9108-1d19d919c400 · outbound

This paper cites Solving Quantitative Reasoning Problems with Language Models.

How Far Are We from Optimal Reasoning Efficiency? Solving Quantitative Reasoning Problems with Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.305178Z

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Observation 86b8cbe4-6afc-4a59-89fe-b898730394fb · outbound

This paper cites LIMR: Less is More for RL Scaling.

How Far Are We from Optimal Reasoning Efficiency? LIMR: Less is More for RL Scaling

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.467346Z

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Observation 2be6c0ee-0ca8-4e3e-abbc-00a4b245e895 · outbound

This paper cites ThinkSwitcher: When to Think Hard, When to Think Fast.

How Far Are We from Optimal Reasoning Efficiency? ThinkSwitcher: When to Think Hard, When to Think Fast

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.597126Z

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Observation 1a1662d7-9fd7-4836-a74c-7d216dbed935 · outbound

This paper cites Reward-Guided Speculative Decoding for Efficient LLM Reasoning.

How Far Are We from Optimal Reasoning Efficiency? Reward-Guided Speculative Decoding for Efficient LLM Reasoning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.717578Z

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Observation 29cdb302-a971-4b8b-8d35-b197d55327d9 · outbound

This paper cites Can Language Models Learn to Skip Steps?.

How Far Are We from Optimal Reasoning Efficiency? Can Language Models Learn to Skip Steps?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.874631Z

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Unavailable: canonical work link unavailable.

Observation 602394ee-2587-4421-b319-6b919e1f567d · outbound

This paper cites Fin-r1: A large language model for financial reasoning through reinforcement learning, 2025.

How Far Are We from Optimal Reasoning Efficiency? Fin-r1: A large language model for financial reasoning through reinforcement learning, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:36.987668Z

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Observation 58910aff-413b-4669-83d8-436907fc1c4c · outbound

This paper cites AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning.

How Far Are We from Optimal Reasoning Efficiency? AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.109195Z

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Observation 1ebbfb26-892d-4476-a44f-5c1083f4cc91 · outbound

This paper cites O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning.

How Far Are We from Optimal Reasoning Efficiency? O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.215102Z

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Observation b0453166-e802-4e6b-93b9-798552d70296 · outbound

This paper cites Deepcoder: A fully open-source 14b coder at o3-mini level.

How Far Are We from Optimal Reasoning Efficiency? Deepcoder: A fully open-source 14b coder at o3-mini level

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:42.236363Z

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 c377da9b-340c-492d-9b00-f5ca907016bb · outbound

This paper cites Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica.

How Far Are We from Optimal Reasoning Efficiency? Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.401902Z

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Observation 4597f838-16df-4b7d-875d-2b572aa48740 · outbound

This paper cites CoT-Valve: Length-Compressible Chain-of-Thought Tuning.

How Far Are We from Optimal Reasoning Efficiency? CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.540313Z

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Observation 7f0e3b78-4b8a-4a0e-b1d8-301a544ca41d · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024.

How Far Are We from Optimal Reasoning Efficiency? Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.684760Z

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Observation b0bfffb9-fc19-4282-bd12-c6c407233330 · outbound

This paper cites s1: Simple test-time scaling.

How Far Are We from Optimal Reasoning Efficiency? s1: Simple test-time scaling

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.783145Z

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Observation 5fb48e33-8d90-4743-a1d6-bc5beb2fc1aa · outbound

This paper cites Self-Training Elicits Concise Reasoning in Large Language Models.

How Far Are We from Optimal Reasoning Efficiency? Self-Training Elicits Concise Reasoning in Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:37.914205Z

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Observation c885bb26-d3fa-4261-b1ed-9b76950c1eab · outbound

This paper cites Learning to reason with llms.

How Far Are We from Optimal Reasoning Efficiency? Learning to reason with llms

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:42.041410Z

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 30c26e1a-4e14-4d49-b0b5-ba9f08e06c7d · outbound

This paper cites THOUGHTTERMINATOR: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models.

How Far Are We from Optimal Reasoning Efficiency? THOUGHTTERMINATOR: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.203391Z

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Observation 12d3f2f4-1a96-445b-97b0-651bbd5e1875 · outbound

This paper cites Optimizing anytime reasoning via budget relative policy optimization, 2025.

How Far Are We from Optimal Reasoning Efficiency? Optimizing anytime reasoning via budget relative policy optimization, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.275527Z

Source-reported events for the cited work

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Observation 3b642e89-0914-4e15-873d-ee3d7fab022d · outbound

This paper cites Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning.

How Far Are We from Optimal Reasoning Efficiency? Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.378369Z

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Observation b79cc64b-1687-40a8-aa04-422e5c900707 · outbound

This paper cites Areal: Ant reasoning rl.

How Far Are We from Optimal Reasoning Efficiency? Areal: Ant reasoning rl

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:41.896795Z

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 1a5c1ea8-2935-407c-8dd8-4cc7c720bb0e · outbound

This paper cites Hawkeye:Efficient Reasoning with Model Collaboration.

How Far Are We from Optimal Reasoning Efficiency? Hawkeye:Efficient Reasoning with Model Collaboration

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.520326Z

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Observation 881669e3-6609-42a6-a6ce-41f8cef58788 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

How Far Are We from Optimal Reasoning Efficiency? VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.588973Z

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Observation da7c21a4-0088-49f6-9cba-0772561ccad2 · outbound

This paper cites Dast: Difficulty-adaptive slow-thinking for large reasoning models.

How Far Are We from Optimal Reasoning Efficiency? Dast: Difficulty-adaptive slow-thinking for large reasoning models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.672014Z

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Observation f44f5bda-0b92-4924-abc8-e3dc9bcced9a · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

How Far Are We from Optimal Reasoning Efficiency? HybridFlow: A Flexible and Efficient RLHF Framework

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.781973Z

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Observation 1ea4eec6-f2da-4d76-8c2c-e7ede3698bc7 · outbound

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

How Far Are We from Optimal Reasoning Efficiency? Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.874482Z

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Observation 33a60b7a-8803-4660-9fd1-0d1350d14818 · outbound

This paper cites Fast Best-of-N Decoding via Speculative Rejection.

How Far Are We from Optimal Reasoning Efficiency? Fast Best-of-N Decoding via Speculative Rejection

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:38.967912Z

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Observation cc5e8e5c-9620-4805-8292-b4227d436795 · outbound

This paper cites Confidence improves self-consistency in llms.arXiv preprint arXiv:2502.06233, 2025.

How Far Are We from Optimal Reasoning Efficiency? Confidence improves self-consistency in llms.arXiv preprint arXiv:2502.06233, 2025

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.034718Z

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Observation 62567a75-a81b-4646-a49a-eddfb5bd2f03 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

How Far Are We from Optimal Reasoning Efficiency? Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.110881Z

Source-reported events for the cited work

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Observation c2579418-fe62-44ed-a47a-5b335be1e611 · outbound

This paper cites Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl,.

How Far Are We from Optimal Reasoning Efficiency? Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:41.736320Z

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 71bb0eea-045e-40b4-be43-5834fba86634 · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models.

How Far Are We from Optimal Reasoning Efficiency? Self-consistency improves chain of thought reasoning in language models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.336776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6eb5b31-48c2-4dc8-a33c-42347b1f8cf9 · outbound

This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

How Far Are We from Optimal Reasoning Efficiency? Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.427926Z

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Observation 50582151-68b1-42f7-aeed-02770b8720a5 · outbound

This paper cites Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2025.

How Far Are We from Optimal Reasoning Efficiency? Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2025

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.495400Z

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Observation f1a8cdf3-9093-43e4-b5df-b24053add6fd · outbound

This paper cites Scalable Chain of Thoughts via Elastic Reasoning.

How Far Are We from Optimal Reasoning Efficiency? Scalable Chain of Thoughts via Elastic Reasoning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.585269Z

Source-reported events for the cited work

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Observation 66b4e1d8-cb54-4e43-bcdc-9bba618053fe · outbound

This paper cites Qwen3 Technical Report.

How Far Are We from Optimal Reasoning Efficiency? Qwen3 Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.672165Z

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Observation 69f3e7db-693f-42a6-82b8-c1ec8f1dc793 · outbound

This paper cites Think When You Need: Self-Adaptive Chain-of-Thought Learning.

How Far Are We from Optimal Reasoning Efficiency? Think When You Need: Self-Adaptive Chain-of-Thought Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.753920Z

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Observation bd9e2959-fb9d-4430-8ac5-4ba499f62c51 · outbound

This paper cites Towards thinking-optimal scaling of test-time compute for llm reasoning, 2025.

How Far Are We from Optimal Reasoning Efficiency? Towards thinking-optimal scaling of test-time compute for llm reasoning, 2025

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.840510Z

Source-reported events for the cited work

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Observation 6af1f9e4-a779-45f5-a6d3-8a07b84ae273 · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

How Far Are We from Optimal Reasoning Efficiency? Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.909550Z

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Observation 589595c2-21f7-4948-a720-d00509f74398 · outbound

This paper cites FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training.

How Far Are We from Optimal Reasoning Efficiency? FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.978220Z

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Observation 3def0778-7059-489c-b93e-13253303564e · outbound

This paper cites Distilling System 2 into System 1.

How Far Are We from Optimal Reasoning Efficiency? Distilling System 2 into System 1

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:40.051297Z

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Observation 6322f138-1f81-47ad-8802-2bdb1a21490b · outbound

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

How Far Are We from Optimal Reasoning Efficiency? DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:40.129855Z

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Observation da1c3027-fe12-44b0-9033-217c568dacd6 · outbound

This paper cites Z1: Efficient Test-time Scaling with Code.

How Far Are We from Optimal Reasoning Efficiency? Z1: Efficient Test-time Scaling with Code

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:40.227034Z

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Observation 487a986d-c299-4028-900a-37dcdba052b3 · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

How Far Are We from Optimal Reasoning Efficiency? VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:40.307416Z

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Observation db605667-f43e-46e3-a2bf-6367e64205fb · outbound

This paper cites AdaptThink: Reasoning Models Can Learn When to Think.

How Far Are We from Optimal Reasoning Efficiency? AdaptThink: Reasoning Models Can Learn When to Think

Reference 58

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This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

How Far Are We from Optimal Reasoning Efficiency? R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Reference 59

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Observation b26ff1b8-67af-4eb3-8ce0-a5385bfacbe9 · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Programs.

How Far Are We from Optimal Reasoning Efficiency? SGLang: Efficient Execution of Structured Language Model Programs

Reference 60

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Observation 29e8489d-c029-4956-befa-11f8158d156d · outbound

This paper cites an unresolved cited work.

How Far Are We from Optimal Reasoning Efficiency? Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:39.249076Z

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Pith citing papers

Observation c1c4647e-b274-4e3b-8244-f619c76f0c3b · inbound

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey How Far Are We from Optimal Reasoning Efficiency?

Reference 53

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Observation 8549c248-218b-49cd-b042-681bf3ad756c · inbound

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models cites this paper.

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models How Far Are We from Optimal Reasoning Efficiency?

Reference 6

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