Paper Citation Record · LEDGER

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 16 inbound Pith citation observations for arXiv:2505.15612.

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

pith.paper-citation-record.v1
2505.15612 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:18:04.508009Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:08.292468Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:19:43.881659Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy5
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28e84fc4-fc33-4282-ba75-0814213730e5 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:17:59.467282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cdb23a8-2f9d-4a88-b874-010ff7f73628 · outbound

This paper cites Arora and A.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Arora and A

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:17:59.568317Z

Source-reported events for the cited work

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Observation eee27329-6959-4997-b431-f94e55efbdc8 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:17:59.667035Z

Source-reported events for the cited work

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Observation ad411e10-4157-4f26-81fe-82d48ecf5652 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:17:59.798415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95b32e92-0252-450b-8771-e9bd405a767c · outbound

This paper cites Gandhi, A.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Gandhi, A

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:07.683827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3d3cf30e-68a5-4ad6-8741-267b5a3249cb · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Training Large Language Models to Reason in a Continuous Latent Space

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.003763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b40c05d-5a62-4179-9807-fb0ab96c2062 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.102621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af46f05c-9118-4372-a39c-0345ca05e32a · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Measuring Massive Multitask Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.199909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b241c72e-f219-4882-a4c3-24328accca6a · outbound

This paper cites Hendrycks, C.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Hendrycks, C

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:07.412863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b913ec90-d3c6-4440-b6c0-178707ad69a4 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.357524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd11b564-bd11-4335-97e6-3cf7d9d1af9d · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.496831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 954cf2c1-8e1d-48fd-b239-df1e25e27ff8 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:18:06.995684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1d8b928e-392b-4957-aeb1-1c5fee32c0aa · outbound

This paper cites s1: Simple test-time scaling.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping s1: Simple test-time scaling

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.725959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 288839eb-69ee-4030-a74f-913083a2f4e8 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Self-Training Elicits Concise Reasoning in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.832579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d730ab99-18ba-4bbf-b24a-fe68527707d4 · outbound

This paper cites OpenAI o1 System Card.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping OpenAI o1 System Card

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:00.974743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c36ddf6-0e6d-4bf7-b3dd-4867468d26a6 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.079263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d08d321-e896-4279-95d7-dd0755e96a78 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.206010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27b57e0-8994-4bc7-874c-23c3118b2407 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.330227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb88eb2-64be-4b8b-a1f7-82935f1ad521 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Proximal Policy Optimization Algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.437191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35381598-08c1-420a-bc5c-2d6a5cc66f97 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.525978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3775df-960a-4b0a-baaf-26fe0d688ef5 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping HybridFlow: A Flexible and Efficient RLHF Framework

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.639730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 474c8158-6c00-4b95-a7e6-e9f2fa52867b · outbound

This paper cites Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.733588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a519d24-8e85-4981-8899-c72fa7b103f2 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:18:06.594553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bcae8ac7-cbd8-45d8-8249-037a2be47198 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:01.984634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 515b6d5c-4d55-4420-a9a5-dfc1e798df75 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:18:06.205324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 20148de8-f039-471e-bae1-d6fbfbd23660 · outbound

This paper cites an unresolved cited work.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:18:05.874359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 41384598-14da-41dc-ae07-b941ab3cd98d · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:02.954245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dfbb9aa-1fd5-4817-b7d3-6c317d6bb337 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:03.643174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a01d5355-f674-45c6-923f-75fd472e93ab · outbound

This paper cites <think>...</think>.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping <think>...</think>

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:05.676753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 577e7356-dde9-457a-becc-c5fcc438a479 · outbound

This paper cites Wait, subtracting a negative is like adding the positive, so that would be 3 + 6, which is 9.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Wait, subtracting a negative is like adding the positive, so that would be 3 + 6, which is 9

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:05.438832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 01a5be31-685e-4785-a785-45382d5c0368 · outbound

This paper cites Calculate $ f(-1) $:\[f(-1) = \frac{3(-1) - 2}{-1 - 2} = \frac{-3 - 2}{-3} = \frac{-5}{-3} = \frac{5}{3}\]3.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Calculate $ f(-1) $:\[f(-1) = \frac{3(-1) - 2}{-1 - 2} = \frac{-3 - 2}{-3} = \frac{-5}{-3} = \frac{5}{3}\]3

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:05.150909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e37737d0-0717-4936-91d9-e1195dd1ca07 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:02.183957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80ecce7-12be-4b1d-ab64-ae428b6d2493 · outbound

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

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:02.380737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8215d2d0-315b-4add-9aa8-a54da7b0598d · inbound

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

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 116

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:29:57.440468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e2806cf4-e1a3-4291-8d88-34263ede61be · inbound

Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It cites this paper.

Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:08.292468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cb0dbfa-7cc7-4266-9804-628e6c489f8c · inbound

Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning cites this paper.

Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T05:14:37.167029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6db48e9a-15f4-4c36-8b2f-f582bbe38f7d · inbound

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization cites this paper.

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:31:55.906712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 57e59cd1-dfe9-492f-b79f-d81a8bceeacb · inbound

Numerically Optimizing Shortcuts to Adiabaticity: A Hybrid Control Strategy cites this paper.

Numerically Optimizing Shortcuts to Adiabaticity: A Hybrid Control Strategy Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T14:28:35.916911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d3c71b-ce7b-40f6-9879-0ea2168f9d51 · inbound

T$^2$PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning cites this paper.

T$^2$PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:45:22.135373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation decffc5e-5dc7-49d2-9828-01f11c1e8ba2 · inbound

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost cites this paper.

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 244

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:06:09.969293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 621aa321-98df-4cf1-8d43-93fc91d81e1d · inbound

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training cites this paper.

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:36:00.027949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1bd3322e-6d0f-47ed-aae6-2d7d2b22e475 · inbound

AIPO: Learning to Reason from Active Interaction cites this paper.

AIPO: Learning to Reason from Active Interaction Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:06:31.370255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b3bda08-724c-48d2-8257-5898f42dd122 · inbound

AIPO: Learning to Reason from Active Interaction cites this paper.

AIPO: Learning to Reason from Active Interaction Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:07:42.254204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0f8f4104-ab31-4fd6-a7e2-35d3b24f9e7b · inbound

LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models cites this paper.

LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:31:16.776375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9473f81e-b1c1-4bbf-b0f4-d52050b385e9 · inbound

CLORE: Content-Level Optimization for Reasoning Efficiency cites this paper.

CLORE: Content-Level Optimization for Reasoning Efficiency Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:51:08.161581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f010621-c287-4485-9c86-3830856bcd32 · inbound

DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning cites this paper.

DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:33:59.250975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c6993438-c84d-4363-b3cd-d682e4cf7a33 · inbound

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning cites this paper.

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:32:44.426841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d4422fa3-a96f-48cf-aaa0-9b986e39183d · inbound

CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs cites this paper.

CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:31.421204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5c542a3d-e8a9-416e-a70b-6da1d6e9e7af · inbound

Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards cites this paper.

Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:19:43.883814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.