Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.17888.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T11:51:02.853733Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T01:58:29.018876Z
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 eb4f74b2-c22a-4b6a-83d3-dcbe42da8bf0 · inbound
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response cites this paper.
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:51:02.853733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e56eae66-2a65-4723-84a4-b2a9ea64b870 · inbound
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring cites this paper.
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T21:06:55.935526Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b0880f39-3b93-4bdc-aa5d-90bb0e6fabbc · inbound
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T23:03:44.623807Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 457bc523-1a1f-400a-9b91-3473eb7f2ab3 · inbound
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models cites this paper.
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 129
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:19:02.275246Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb7706c2-0dea-40de-8ebd-6afbeaf1cdb6 · inbound
Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning cites this paper.
Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:56:02.522980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a6abd05-fefb-4533-b552-117d5eed47b8 · inbound
On the Nature of Regularity Assumptions in Bilevel Optimization with Constrained Lower-level Problem cites this paper.
On the Nature of Regularity Assumptions in Bilevel Optimization with Constrained Lower-level Problem Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:58:29.020663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.