Paper Citation Record · LEDGER

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2502.06358.

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

pith.paper-citation-record.v1
2502.06358 v3

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:45:06.723194Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy8
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3af19425-ff16-49a3-8c6e-16da01b46442 · outbound

This paper cites an unresolved cited work.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.644785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55672e0d-51e7-4a6f-8e29-841db309bf0d · outbound

This paper cites Advances in neural information processing systems 33, 1877–1901 (2020).

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Advances in neural information processing systems 33, 1877–1901 (2020)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.650434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c8d2db2-8ed6-49c3-b1e8-073838a400e9 · outbound

This paper cites In: Pro- ceedings of the 41st International Conference on Machine Learning.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL In: Pro- ceedings of the 41st International Conference on Machine Learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.994954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0c285e4b-dbe2-4698-bed8-9bc5b2f3b782 · outbound

This paper cites Advances in neural information processing systems 34, 15084–15097 (2021).

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Advances in neural information processing systems 34, 15084–15097 (2021)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.979175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19de3897-f4e1-449d-a78b-5428816974d4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.664094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e45d8454-0b7a-4fda-9e05-1942449a1d68 · outbound

This paper cites Prompt-Tuning Decision Transformer with Preference Ranking.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Prompt-Tuning Decision Transformer with Preference Ranking

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T15:45:06.834664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e539d1b3-3cea-4ede-9228-c61c018ebfd0 · outbound

This paper cites Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T15:45:06.817795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35363cb2-2fcb-4cff-ad02-40fea60c53d7 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.678382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b32bd56-4a63-4e7a-8556-f8a6510cf7d8 · outbound

This paper cites In: Proceedings of the 19th international conference on World wide web.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL In: Proceedings of the 19th international conference on World wide web

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.964480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 69076c5d-e0ee-4c73-9c58-e81c39d8c0ab · outbound

This paper cites A Survey on Transformers in Reinforcement Learning.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL A Survey on Transformers in Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.686947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dd92499-d746-4615-b3e9-4e26817e8b6f · outbound

This paper cites In: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following (2023).

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL In: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following (2023)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.951706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28cc83a4-6f68-4916-bd93-760a4bc7f956 · outbound

This paper cites an unresolved cited work.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.693831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f7f0a08-8380-4313-91cf-64cb795bbaa6 · outbound

This paper cites In: International conference on machine learning.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL In: International conference on machine learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.931576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 973e510e-2489-47bc-a3e2-9e55f2b6491a · outbound

This paper cites Transactions on Machine Learning Research.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Transactions on Machine Learning Research

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.917946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 729f4359-cd15-462c-a47f-ef6f3244ff5c · outbound

This paper cites Proximal Policy Optimization Algorithms.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Proximal Policy Optimization Algorithms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.704519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34609c7c-b00a-4ba9-8270-16502d9aa66e · outbound

This paper cites an unresolved cited work.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:45:06.905417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e240516-5ea6-4089-99c3-efbd86510ea0 · outbound

This paper cites Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T15:45:06.712104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280d3f35-198c-4247-9d39-a00913607638 · outbound

This paper cites an unresolved cited work.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:45:06.891997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bab4f885-62de-4eb7-b423-6b28d0a3b359 · outbound

This paper cites In: in- ternational conference on machine learning.

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL In: in- ternational conference on machine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.876398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1b76e480-d7d0-40f2-8ac7-e843370c5502 · outbound

This paper cites Advances in Neural Information Processing Systems37, 55086–55114 (2024).

Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL Advances in Neural Information Processing Systems37, 55086–55114 (2024)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:45:06.858870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.