Paper Citation Record · LEDGER

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning?

As of 13 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2509.03790.

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

pith.paper-citation-record.v1
2509.03790 v2

Coverage vector

measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 61a69c04-57ad-4741-9361-5d42d3ce38c7 · outbound

This paper cites An optimistic perspective on offline reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? An optimistic perspective on offline reinforcement learning

Reference 1

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This paper cites Concrete problems in ai safety.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Concrete problems in ai safety

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:55.206844Z

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This paper cites Minimax regret bounds for reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Minimax regret bounds for reinforcement learning

Reference 3

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This paper cites Never give up: Learning directed exploration strategies.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Never give up: Learning directed exploration strategies

Reference 4

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This paper cites Successor features for transfer learning in reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Successor features for transfer learning in reinforcement learning

Reference 5

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This paper cites Recent advances in hierarchical reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Recent advances in hierarchical reinforcement learning

Reference 6

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This paper cites Unifying count-based exploration and hashing: A case study of model-based rl.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Unifying count-based exploration and hashing: A case study of model-based rl

Reference 7

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This paper cites Exploration by random network distillation.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Exploration by random network distillation

Reference 8

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This paper cites Exact matrix completion via convex optimization.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Exact matrix completion via convex optimization

Reference 9

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This paper cites Deep reinforcement learning from human preferences.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Deep reinforcement learning from human preferences

Reference 10

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Observation 3436a895-2f50-435a-b202-d04e798b8df2 · outbound

This paper cites Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning

Reference 11

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Observation f0aa382e-e0ea-4f29-ae8c-83008e5ca69f · outbound

This paper cites First return, then explore.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? First return, then explore

Reference 12

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Observation aaaba82b-867b-4c85-b801-27dd68042aba · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Model-agnostic meta-learning for fast adaptation of deep networks

Reference 13

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This paper cites Noisy networks for exploration.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Noisy networks for exploration

Reference 14

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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 15

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This paper cites Selective classification for deep neural networks.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Selective classification for deep neural networks

Reference 16

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This paper cites On calibration of modern neural networks.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? On calibration of modern neural networks

Reference 17

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This paper cites Is q-learning provably efficient? In Advances in neural information processing systems, pp.\ 4863--4873, 2018.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Is q-learning provably efficient? In Advances in neural information processing systems, pp.\ 4863--4873, 2018

Reference 18

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This paper cites Matrix factorization techniques for recommender systems.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Matrix factorization techniques for recommender systems

Reference 19

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Conservative q-learning for offline reinforcement learning

Reference 20

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 21

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This paper cites Curl: Contrastive unsupervised representations for reinforcement learning.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Curl: Contrastive unsupervised representations for reinforcement learning

Reference 22

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Scalable agent alignment via reward modeling: a research direction

Reference 23

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Neural matrix completion

Reference 24

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Geometric deep learning on graphs and manifolds using mixture model cnns

Reference 25

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Deep exploration via bootstrapped dqn

Reference 26

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This paper cites Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift.

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift

Reference 27

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Curiosity-driven exploration by self-supervised prediction

Reference 28

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Parameter space noise for exploration

Reference 29

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Masked autoencoder for distribution estimation

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization

Reference 31

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Proximal policy optimization algorithms

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Data-efficient reinforcement learning with self-predictive representations

Reference 33

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Reinforcement learning: An introduction

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Exploration: A study of count-based exploration for deep reinforcement learning

Reference 35

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Transfer learning for reinforcement learning domains: A survey

Reference 36

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 37

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? write newline

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? @esa (Ref

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Unresolved cited work

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What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? Unresolved cited work

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Pith citing papers

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