Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:18:04.508009Z
Paper Citation Record · LEDGER
Learn to Reason Efficiently with Adaptive Length-based Reward Shaping
As of 10 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:08.292468Z
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
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.