Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:30:38.148313Z
Paper Citation Record · LEDGER
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
As of 20 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 30 inbound Pith citation observations for arXiv:2506.19767.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:52:15.303331Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
25 of 25 outbound references displayed
- verified exact0
- verified fuzzy5
- unresolved20
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation cbe01040-8176-406a-b45b-134de8acc13c · outbound
This paper cites The magnitude of the decrease for each non-target tokenvis proportional to its current probabilityπ θ(v|x,y<t).
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning The magnitude of the decrease for each non-target tokenvis proportional to its current probabilityπ θ(v|x,y<t)
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T18:30:38.558327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 19f55063-3ef4-4662-b74f-643205027fcd · outbound
This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.045826Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7775247c-7ce1-4249-84ee-9ace25ee397e · outbound
This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.050472Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a57d2fe3-68d0-4906-9adc-01e65938bc09 · outbound
This paper cites Improving RL Exploration for LLM Reasoning through Retrospective Replay.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Improving RL Exploration for LLM Reasoning through Retrospective Replay
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.055667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5cf16aa3-009e-4a19-9e08-92cbd05491ec · outbound
This paper cites LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.060670Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5dc7a3d8-dbd3-47b7-8823-1fc9362047f5 · outbound
This paper cites How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.065791Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65877c37-2906-48b1-8829-8017d612a744 · outbound
This paper cites Learning to Reason under Off-Policy Guidance.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Learning to Reason under Off-Policy Guidance
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.070952Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c0783268-72c6-4f8e-8038-25ed0fae6a5b · outbound
This paper cites TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.074895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e18dc3a-2a3c-4b87-9f02-c016dfab8075 · outbound
This paper cites UFT: Unifying supervised and reinforcement fine-tuning.arXiv preprint arXiv:2505.16984, 2025a.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning UFT: Unifying supervised and reinforcement fine-tuning.arXiv preprint arXiv:2505.16984, 2025a
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.078831Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad07a096-01e3-40e2-abf5-7afca31de710 · outbound
This paper cites 5, 22 Lu Ma, Hao Liang, Meiyi Qiang, Lexiang Tang, Xiaochen Ma, Zhen Hao Wong, Junbo Niu, Chengyu Shen, Runming He, Bin Cui, et al.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning 5, 22 Lu Ma, Hao Liang, Meiyi Qiang, Lexiang Tang, Xiaochen Ma, Zhen Hao Wong, Junbo Niu, Chengyu Shen, Runming He, Bin Cui, et al
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.092173Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aeeb7730-bc28-48cb-8a7d-8a72a80e125d · outbound
This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Measuring Mathematical Problem Solving With the MATH Dataset
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.100595Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 84bf90af-a3eb-4d56-8eea-75b646a479d1 · outbound
This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.109768Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e80430f-21f4-4522-9a3c-1321d14f7aeb · outbound
This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.114032Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 426a2e7a-f3f8-4cec-8b43-72664517a6fa · outbound
This paper cites Process Reinforcement through Implicit Rewards.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Process Reinforcement through Implicit Rewards
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.118345Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4b4cafe-138b-4da1-b556-255e515f2772 · outbound
This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.122778Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7c0839fc-e534-46bd-b906-90277b620ff8 · outbound
This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Understanding R1-Zero-Like Training: A Critical Perspective
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.127553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f77ef34b-767b-4b0f-8fd2-74e212b73360 · outbound
This paper cites For datasets with limited sample sizes (AIME24 and AMC), we report the avg@32 metric; for the remaining three datasets, we adopt pass@1 as the evaluation criterion.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning For datasets with limited sample sizes (AIME24 and AMC), we report the avg@32 metric; for the remaining three datasets, we adopt pass@1 as the evaluation criterion
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T18:30:38.601772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 823de888-79d3-4a9f-bb10-161813419857 · outbound
This paper cites <think>\n {thoughts}</think>\n.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning <think>\n {thoughts}</think>\n
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T18:30:38.586601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 147acd18-44e4-4828-abf0-79a993c358e8 · outbound
This paper cites Google-proof.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Google-proof
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T18:30:38.572625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 41f8982b-c858-4a83-8a96-bd5958b82045 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.083143Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf1c6665-0cd0-457f-aa09-037c426ab0b7 · outbound
This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.105293Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe7424f0-86da-4f18-b9f3-6817648067c8 · outbound
This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.096229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28f53627-385a-43b0-84cc-c2ce833bc023 · outbound
This paper cites 17 A.2 Entropy-aware Gradient Clipping.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning 17 A.2 Entropy-aware Gradient Clipping
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T18:30:38.616635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ff13ae6f-403c-4c05-8609-00d8adfe5d6f · outbound
This paper cites Proximal Policy Optimization Algorithms.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning Proximal Policy Optimization Algorithms
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.087653Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f5e7844-bbd9-4cb7-9fa5-b754265c7a04 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:30:38.040628Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 9ce80c5e-b8d2-4b1e-8cb6-e72c91dd252f · inbound
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 193
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T08:41:23.526899Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6c3f19e7-c0d6-459f-924c-eb6dc8dd69a3 · inbound
Post-Completion Learning for Language Models cites this paper.
Post-Completion Learning for Language Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:52:15.303331Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f2dbca8-2b50-4732-998c-73f84649e9bb · inbound
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning cites this paper.
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T23:56:55.045505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f9961d65-2c5c-4622-923a-f528de674486 · inbound
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning cites this paper.
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:49:33.169961Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e36dad10-0739-4a45-b78b-9b7028f14b24 · inbound
Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration cites this paper.
Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T22:36:53.701052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2d21066e-700b-4b6d-a68f-377c3c099a3a · inbound
A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.
A Survey of Reinforcement Learning for Large Reasoning Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 148
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T00:02:25.405749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 975e4c8d-006c-480c-b21b-c252f9e63794 · inbound
CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems cites this paper.
CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T14:42:33.241269Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5c519e1-9d20-4eb9-af14-6b057420aa87 · inbound
AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models cites this paper.
AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:58.176602Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a44f1f16-1fbc-4dc5-bee6-5a444af496a9 · inbound
Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts cites this paper.
Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T10:39:26.597119Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95a5c8a6-8a44-46ff-b8ce-25da8195f68b · inbound
RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning cites this paper.
RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T09:33:38.707015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 336fcfdd-e5d7-437a-87d2-d788895f128a · inbound
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models cites this paper.
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:15:50.322424Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50b95321-bffc-40e8-867f-d8c974c23178 · inbound
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning cites this paper.
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T22:43:37.638943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5853afe1-a468-491f-b111-8e395ab4a9a5 · inbound
Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models cites this paper.
Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T05:28:11.662347Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b00e54c2-0f98-4160-835d-a032820f5cf6 · inbound
Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings cites this paper.
Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T12:45:37.356079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 587b056d-a5f2-41cc-93fa-1a211b8b1ed3 · inbound
$\pi$-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data cites this paper.
$\pi$-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:45:27.829704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3b331609-45b2-472c-b100-ac69b4472109 · inbound
Near-Future Policy Optimization cites this paper.
Near-Future Policy Optimization SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T00:54:48.662477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7c4bd75f-8c56-4966-a086-5eedfcbb4d73 · inbound
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors cites this paper.
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:51:44.011351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 285b16b3-efde-4ca8-89b8-1e5350cd1871 · inbound
AIPO: Learning to Reason from Active Interaction cites this paper.
AIPO: Learning to Reason from Active Interaction SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T08:06:31.724069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 83613a1d-44e0-467f-8c7c-ef40c35fc3ac · inbound
AIPO: Learning to Reason from Active Interaction cites this paper.
AIPO: Learning to Reason from Active Interaction SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T18:07:42.235985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 859ffaa4-7137-4c9d-bc2d-2b543d12052e · inbound
Learning Agentic Policy from Action Guidance cites this paper.
Learning Agentic Policy from Action Guidance SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:07:17.645623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 96a80a11-f43d-46f4-a21d-eb95fc43ec92 · inbound
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance cites this paper.
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T06:21:10.377765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c60b7a7d-435d-4f00-bc9e-a0ce27f0fe67 · inbound
GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training cites this paper.
GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:34:01.719169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 21f8c1db-3f48-42ea-9da4-fb704c60b231 · inbound
Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models cites this paper.
Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:03:13.496547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 967da951-9120-4295-8a7c-68731a45b4d7 · inbound
What are Key Factors for Updates in RL for LLM Reasoning? cites this paper.
What are Key Factors for Updates in RL for LLM Reasoning? SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T09:09:43.590285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ee13d56a-e9c9-4aa0-9614-253e730673ff · inbound
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning cites this paper.
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T16:29:56.991869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 94116e2e-ed12-4903-acf6-fe4865d5320d · inbound
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning cites this paper.
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 64
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T13:26:58.442808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 151eb895-fbf2-4d7d-ba72-a03c726fcb31 · inbound
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples cites this paper.
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T11:23:30.063231Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7b7907b-ccc5-4b9b-be3f-b7943ba9f25f · inbound
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples cites this paper.
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:23:28.545828Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b13a36df-6e61-4002-bd65-f7408ccdc670 · inbound
Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs cites this paper.
Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:13:48.101782Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66463944-db0e-4f81-8a74-1a8135b269ac · inbound
SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs cites this paper.
SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T00:51:24.937566Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.