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Paper Citation Record · LEDGER

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.22854.

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

pith.paper-citation-record.v1
2507.22854 v3

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:22:57.874474Z

measured 16 of 16 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

16 of 16 outbound references displayed

  • verified exact1
  • verified fuzzy10
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 75fe3cf3-5c90-4468-bd2e-87fc122f11db · outbound

This paper cites Markov decision processes with their applications , vol- ume 14.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Markov decision processes with their applications , vol- ume 14

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T11:22:57.842621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:57.842621Z digest=sha256:42beb0b8e51ef62dc206627a629fa8653abe815e094d6320cc406a0b48de1c77

Observation 4818222d-efd6-4c52-9fad-972f49f8c88e · outbound

This paper cites Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T11:22:57.846867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:57.846867Z digest=sha256:ff07384cf955c6c78e556c26584e1e6853cd1f3307629932c2e36d840528b6ee

Observation de05e2e0-113e-441b-9c5e-e193260455b9 · outbound

This paper cites Almost optimal model-free reinforce- ment learningvia reference-advantage decomposition.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Almost optimal model-free reinforce- ment learningvia reference-advantage decomposition

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.102079Z

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.

source=pdf_text observed=2026-08-06T11:22:57.874474Z digest=sha256:45d7473110479ffd1b3753de48b245ca8bdf2f858f732cafcd4871d8b6ba4d1e

Observation 67e72fbd-9ef6-47f6-9b05-2f1f3fffb0e4 · outbound

This paper cites A Quantum Algorithm for Finding the Minimum.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model A Quantum Algorithm for Finding the Minimum

Reference 1998

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unresolved
no resolver link, observed 2026-08-06T11:22:57.829026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:57.829026Z digest=sha256:9775b5622ce195cb41e617d444286b84693aba632f67958dc3192aec64b439c3

Observation 18e15db9-9115-45ca-9938-caa8c5ed36a4 · outbound

This paper cites Improved Analysis of UCRL2 with Empirical Bernstein Inequality.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Improved Analysis of UCRL2 with Empirical Bernstein Inequality

Reference 2005

Resolution
unresolved
no resolver link, observed 2026-08-06T11:22:57.833805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:57.833805Z digest=sha256:1d43adaef65b02de32ddbc91f92747c2975b75ceaeb0b4cdff8ed5f9086d9ef9

Observation ab7710d8-836a-4d91-a4b9-2db33c708e67 · outbound

This paper cites Minimax regret bounds for reinforcement learning.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Minimax regret bounds for reinforcement learning

Reference 2006

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.190660Z

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.

source=pdf_text observed=2026-08-06T11:22:57.819860Z digest=sha256:adfb20e31915e520be948c690f50b7840fd13b51d4b078c4658d5e7022d1d214

Observation e68d45e1-769e-4625-98ba-b3e184298ee1 · outbound

This paper cites Logarithmic online regret bounds for undiscounted reinforcement learning.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Logarithmic online regret bounds for undiscounted reinforcement learning

Reference 2008

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.204202Z

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.

source=pdf_text observed=2026-08-06T11:22:57.815098Z digest=sha256:7f8d8d1a3921144455d0a6188fd3d10670f0dd8d7b7030d2f763a96b15c4b707

Observation fd105edd-8c00-41f0-9eea-5e0d78751993 · outbound

This paper cites Near-optimal regret bounds for reinforcement learning.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Near-optimal regret bounds for reinforcement learning

Reference 2012

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.217846Z

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.

source=pdf_text observed=2026-08-06T11:22:57.810526Z digest=sha256:7106996f27f1be826c022dca87e6c760e4792ceba30312610192da6183a08d89

Observation 67ae80fc-2a13-42db-b016-b8c2233fdfb5 · outbound

This paper cites Improved regret bounds for undiscounted continuous reinforcement learning.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Improved regret bounds for undiscounted continuous reinforcement learning

Reference 2013

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.146322Z

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.

source=pdf_text observed=2026-08-06T11:22:57.852347Z digest=sha256:0e93d5cdc1ff17b971eb039522eaf929499f9baeb88ea3b0ba5f516fe0df5379

Observation fcb39ba7-a87b-49a5-835d-76a1daa5cb04 · outbound

This paper cites Quantum probability oracles & multidimensional amplitude estimation.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Quantum probability oracles & multidimensional amplitude estimation

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.176021Z

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.

source=pdf_text observed=2026-08-06T11:22:57.824455Z digest=sha256:e86feddde0b6366a178c47ed512d7d1991040cb7c6f3f9438a5743db80bab00c

Observation 0556d1d9-6889-41e9-b3d4-aaf336d63979 · outbound

This paper cites Dynamic policy programming.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Dynamic policy programming

Reference 2018

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verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.231651Z

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.

source=pdf_text observed=2026-08-06T11:22:57.805142Z digest=sha256:c2b1dc9f89b043668dae61690aa55ac358b5493eb608697929476705855b6873

Observation ed7c7f1b-2f63-4f6f-8943-36aa2379ada7 · outbound

This paper cites Hernandez-Lerma.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Hernandez-Lerma

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.160474Z

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.

source=pdf_text observed=2026-08-06T11:22:57.838527Z digest=sha256:a97d0b890b715f441a8123bb3324d23145377c486f5dd2c9412e240a467a4149

Observation f54689f6-5d42-400b-a9bf-f0e1dfc99477 · outbound

This paper cites Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-06T11:22:57.865812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:57.865812Z digest=sha256:a5a943bef138a0e2fc857f0eadedd30add8d40f2374b0ebcc9efbc098db993a8

Observation b77448f8-06d7-44bd-b9a2-9dfae67d3fa4 · outbound

This paper cites Interactive value iteration for Markov decision processes with unknown rewards.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Interactive value iteration for Markov decision processes with unknown rewards

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.117270Z

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.

source=pdf_text observed=2026-08-06T11:22:57.870544Z digest=sha256:d33c92f0303dec2c0bbf94334411d0a0ef9f3474fe801d771329decc8468ee57

Observation f4f8c309-e9e0-4570-b2c3-ad3751b2b498 · outbound

This paper cites Learn- ing infinite-horizon average-reward MDPs with linear function approximation.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Learn- ing infinite-horizon average-reward MDPs with linear function approximation

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:22:58.131921Z

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.

source=pdf_text observed=2026-08-06T11:22:57.861761Z digest=sha256:38b75bfc13d6dfad1377800aac532584f6bd365cccddf75ae91278b71e1d2865

Observation 87020556-fd2f-4104-b40c-d19aa609a1d4 · outbound

This paper cites Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities.

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities

Reference 2024

Resolution
verified exact
local_arxiv, observed 2026-08-06T11:22:57.934217Z

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.

source=pdf_text observed=2026-08-06T11:22:57.856397Z digest=sha256:295a5b7718df88368f0a7da3058e8088dab53d35e261f964dbc7898fa2fbbc67

Pith citing papers

No inbound Pith citation observations are available.