Paper Citation Record · LEDGER

Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2505.13026.

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

pith.paper-citation-record.v1
2505.13026 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:32.507410Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:27:26.462219Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 43b5554e-0bf3-4687-b7f5-aed6cf3c50d0 · inbound

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling cites this paper.

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:40:46.472508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ae68cf3-9cc2-46be-b62b-fa959f8e26cb · inbound

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization cites this paper.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:16:56.993587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 994317d1-da8b-437a-8e2f-ff8730d29f96 · inbound

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning cites this paper.

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-18T23:56:54.953338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d64f13b-c276-4196-aa2a-14bdc9bec298 · 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 Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:32.507410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96c74d77-4f08-43bf-b901-c92e189d6bbc · inbound

SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning cites this paper.

SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:06:14.415252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b9a0335-f9cf-4929-8a2d-e56052ccac1c · inbound

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level cites this paper.

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:01:17.494889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1134a14-495b-415f-8224-ae2b5b3a2183 · inbound

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level cites this paper.

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:20:55.323246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation caf1d41f-2ff2-403c-ae32-e47e3c03e4d7 · inbound

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level cites this paper.

Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:17:59.712736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0028337c-5869-44ff-a2b4-d846267ce88b · inbound

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance cites this paper.

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:19:43.010585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78f0c023-f494-4c08-a0cd-acbfa6d5ac05 · inbound

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution cites this paper.

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-30T00:14:04.520517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81be6abc-94e2-4b28-8412-c180b2b289e9 · inbound

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff cites this paper.

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:27:26.463643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.