Theoretical Foundations of $\max$@$k$ Reinforcement Learning | Paper Citation Record

Observation 625e53fa-f725-4c3a-a903-19d02bb5b830 · outbound

This paper cites Max k-armed bandit: On the extremehunter algorithm and beyond.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Max k-armed bandit: On the extremehunter algorithm and beyond

Reference 1

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Observation 07c00da6-1cb9-440b-9610-ffd8202ec4ab · outbound

This paper cites Model-based reinforcement learning with a generative model is minimax optimal.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Model-based reinforcement learning with a generative model is minimax optimal

Reference 2

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This paper cites Rewarding behaviors.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Rewarding behaviors

Reference 3

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Observation f49fc2c4-5a51-461c-98c8-991e1fc12ad8 · outbound

This paper cites The best of n worlds: Aligning reinforcement learning with best-of-n sampling via max@ k optimisation.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning The best of n worlds: Aligning reinforcement learning with best-of-n sampling via max@ k optimisation

Reference 4

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Observation 8fd909a1-f7e5-4b00-84f3-c6bd2d5cb84b · outbound

This paper cites Optimal rates for feasible payoff set estimation in games.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Optimal rates for feasible payoff set estimation in games

Reference 5

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Observation dc095108-3c1e-42c3-9fbc-567fd533d0f8 · outbound

This paper cites Regret bounds for risk-sensitive reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Regret bounds for risk-sensitive reinforcement learning

Reference 6

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Observation 1b93c90e-f45a-484d-a499-3ff7ac0d225a · outbound

This paper cites Efficient Algorithms for Extreme Bandits.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Efficient Algorithms for Extreme Bandits

Reference 7

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Observation 1eb87b60-46ee-479d-82c1-a39110c44967 · outbound

This paper cites A distributional perspective on reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning A distributional perspective on reinforcement learning

Reference 8

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Observation 922f4659-6ee2-4e0e-beb0-e2f7211de686 · outbound

This paper cites Distributional reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Distributional reinforcement learning

Reference 9

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Observation 562fbd03-df68-4cdf-8a0e-3e55637ccec0 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Graph of thoughts: Solving elaborate problems with large language models

Reference 10

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Observation 76f0cb61-4bc6-43ed-8015-1a75c354c82b · outbound

This paper cites Concentration inequalities.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Concentration inequalities

Reference 11

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Observation bb58fe7f-850f-4322-bbcd-dc3597d8f089 · outbound

This paper cites Ltlf/ldlf non-markovian rewards.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Ltlf/ldlf non-markovian rewards

Reference 12

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Observation 9fdfe351-46f4-495d-8ed8-4d466b2f21d0 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 13

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Observation 0a285c5d-5d23-4918-b36b-cabf9dd18427 · outbound

This paper cites Extreme bandits.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Extreme bandits

Reference 14

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Observation bfeca6e3-9d90-4053-8ae5-038570ccd3c0 · outbound

This paper cites MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling

Reference 15

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Observation 40355f6a-ae50-479c-bbf6-c3036bc00e10 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Evaluating Large Language Models Trained on Code

Reference 16

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Observation 2671270e-a7bf-4526-b5e5-6280f75f798a · outbound

This paper cites Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Reference 17

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Observation 7b5455b1-d8ff-47a6-8215-6104926b509b · outbound

This paper cites Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? Advances in Neural Information Processing Systems, 38: 0 57654--57689, 2026 b.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? Advances in Neural Information Processing Systems, 38: 0 57654--57689, 2026 b

Reference 18

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Observation 8554e31f-2321-4536-b3c6-5be9d4c27a01 · outbound

This paper cites Robust reinforcement learning with general utility.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Robust reinforcement learning with general utility

Reference 19

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Observation a2533bf9-107f-4c86-9d69-6d7ce6a91b78 · outbound

This paper cites Algorithms for cvar optimization in mdps.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Algorithms for cvar optimization in mdps

Reference 20

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Observation aa071919-3714-43e4-b0ad-7743923b2799 · outbound

This paper cites Inference-aware fine-tuning for best-of-n sampling in large language models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Inference-aware fine-tuning for best-of-n sampling in large language models

Reference 21

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Observation 629f9abf-1e80-4559-8c7a-5bf323b82482 · outbound

This paper cites Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

Reference 22

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Observation 8c2f4e4c-eadd-4214-ace6-6e0ff1a60848 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Training Verifiers to Solve Math Word Problems

Reference 23

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Observation e929eea7-8113-492b-9198-5cd1bc497a9d · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 24

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This paper cites Distributional reinforcement learning with quantile regression.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Distributional reinforcement learning with quantile regression

Reference 25

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Observation 6c1b5b99-2c8c-4610-8596-d6b86f17a714 · outbound

This paper cites Sample complexity of episodic fixed-horizon reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Sample complexity of episodic fixed-horizon reinforcement learning

Reference 26

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Observation 71c59548-95e2-44ce-b301-6399e4e4a8ed · outbound

This paper cites Chain-of-verification reduces hallucination in large language models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Chain-of-verification reduces hallucination in large language models

Reference 27

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Observation 0b9aa258-404a-4eb5-9628-71baec40fa45 · outbound

This paper cites Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited

Reference 28

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Observation a6cdc281-69f9-4117-b890-a96d3ca60546 · outbound

This paper cites Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret

Reference 29

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Observation 59853193-3c51-4bf3-8d76-b54f8961256d · outbound

This paper cites Reinforcement learning with non-markovian rewards.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Reinforcement learning with non-markovian rewards

Reference 30

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Observation 6f889ea7-c8bb-4e4e-9bbe-35fafa7a31b9 · outbound

This paper cites Explore first, exploit next: The true shape of regret in bandit problems.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Explore first, exploit next: The true shape of regret in bandit problems

Reference 31

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Observation 7c2ef413-981c-447a-876a-4ad6be31dd22 · outbound

This paper cites Minimax pac bounds on the sample complexity of reinforcement learning with a generative model.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Minimax pac bounds on the sample complexity of reinforcement learning with a generative model

Reference 32

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Observation 668552ad-4f7a-4bb7-9d75-c4183ec9e73e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 33

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This paper cites Provably efficient maximum entropy exploration.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Provably efficient maximum entropy exploration

Reference 34

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Observation 17730fc1-6c16-4a39-a6f1-762ade99ac0d · outbound

This paper cites Reward machines: Exploiting reward function structure in reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Reward machines: Exploiting reward function structure in reinforcement learning

Reference 35

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This paper cites Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought

Reference 36

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This paper cites OpenAI o1 System Card.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning OpenAI o1 System Card

Reference 37

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Observation 90a5d3d3-1c0b-4473-ac97-e58517198755 · outbound

This paper cites Planning in markov decision processes with gap-dependent sample complexity.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Planning in markov decision processes with gap-dependent sample complexity

Reference 38

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Observation 22699b3b-7ddc-46f0-811d-a0fbd40a406e · outbound

This paper cites Policy Gradient for Reinforcement Learning with General Utilities.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Policy Gradient for Reinforcement Learning with General Utilities

Reference 39

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This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 40

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This paper cites Breaking the sample size barrier in model-based reinforcement learning with a generative model.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Breaking the sample size barrier in model-based reinforcement learning with a generative model

Reference 41

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This paper cites Competition-level code generation with alphacode.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Competition-level code generation with alphacode

Reference 42

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Observation 91a9539b-b2f6-4ff7-a710-2cd01fbf50ff · outbound

This paper cites Let's verify step by step.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Let's verify step by step

Reference 43

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Observation aeb5fe5c-079c-4c8b-92b6-50f6800bb1dc · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Self-refine: Iterative refinement with self-feedback

Reference 44

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Observation 2ebbdfa3-1951-496a-8bc0-acc6d8412474 · outbound

This paper cites Challenging common assumptions in convex reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Challenging common assumptions in convex reinforcement learning

Reference 45

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Observation 8adae96a-6142-4b32-9a7b-1b874e16fdcb · outbound

This paper cites Convex reinforcement learning in finite trials.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Convex reinforcement learning in finite trials

Reference 46

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Observation be78362e-7f03-4f6c-b83f-723801e46e75 · outbound

This paper cites No regret bound for extreme bandits.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning No regret bound for extreme bandits

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.103499Z

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Observation 44e5bb33-067d-47de-b63c-0a47e274b126 · outbound

This paper cites Markov decision processes.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Markov decision processes

Reference 48

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Observation e77ccf59-7b37-4a5f-a864-b189e2736d86 · outbound

This paper cites Near-minimax-optimal distributional reinforcement learning with a generative model.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Near-minimax-optimal distributional reinforcement learning with a generative model

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.077284Z

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Observation 16e479be-4ba3-421e-9332-4a85203e27d8 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Reflexion: Language agents with verbal reinforcement learning

Reference 50

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Observation cdf2dc89-efc2-4602-84db-0e7dfcdc4a67 · outbound

This paper cites Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

Reference 51

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Observation 194ae738-564d-4436-b827-29d982c6e385 · outbound

This paper cites Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.050398Z

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Observation ce10b7e7-ec70-4dd4-b0da-719367611f40 · outbound

This paper cites On Advantage Estimates for Max@K Policy Gradients.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning On Advantage Estimates for Max@K Policy Gradients

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:43:00.594510Z

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Observation 29b53253-1846-4525-9ed1-32863b156d96 · outbound

This paper cites Optimizing language models for inference time objectives using reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Optimizing language models for inference time objectives using reinforcement learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.034770Z

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Observation 6c52c959-462b-42b7-8118-b8ff9fddab3f · outbound

This paper cites Finite-Time Regret Analysis of Retry-Aware Bandits.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Finite-Time Regret Analysis of Retry-Aware Bandits

Reference 55

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Observation a3287bdd-5ede-4ab7-bf89-b864090bc05b · outbound

This paper cites Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

Reference 56

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Observation f57f8433-a6f2-46bc-b670-d55b6d5b0f6f · outbound

This paper cites Advancing Mathematics Research with AI-Driven Formal Proof Search.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Advancing Mathematics Research with AI-Driven Formal Proof Search

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.125774Z

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Observation a5459578-ee59-4c28-bdca-e4a58431d1c1 · outbound

This paper cites Recursive self-aggregation unlocks deep thinking in large language models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Recursive self-aggregation unlocks deep thinking in large language models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.130531Z

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Observation 2d1235b0-03ef-4dcb-9484-78253068f15f · outbound

This paper cites Pass@ k policy optimization: Solving harder reinforcement learning problems.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Pass@ k policy optimization: Solving harder reinforcement learning problems

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.018740Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1d36c955-e4b9-4b5a-8877-6cd7884a8c75 · outbound

This paper cites Sample-efficient reinforcement learning for linearly-parameterized mdps with a generative model.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Sample-efficient reinforcement learning for linearly-parameterized mdps with a generative model

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:01.002243Z

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Observation 08746916-6130-4183-a306-b7442a7ecd6f · outbound

This paper cites Near-minimax-optimal risk-sensitive reinforcement learning with cvar.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Near-minimax-optimal risk-sensitive reinforcement learning with cvar

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.143775Z

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Observation d44bf767-cedd-4e67-a821-4dd80d4ce061 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.148168Z

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Observation 0e438ab1-7139-48ec-891d-ff09a2f5fbb6 · outbound

This paper cites Risk-sensitive Markov Decision Process and Learning under General Utility Functions.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Risk-sensitive Markov Decision Process and Learning under General Utility Functions

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.152775Z

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Observation aaaaf2ff-7240-4b6f-b5d1-0bd167f1baa2 · outbound

This paper cites Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.157724Z

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Observation dedb8a22-c4de-48e0-a547-c5498c0a8718 · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.162507Z

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Observation 9fe2c7f4-2c1a-4375-91a7-89e3fa68afdd · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Tree of thoughts: Deliberate problem solving with large language models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.167272Z

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Observation b6238607-8f5f-46ca-8713-11438ee98bd0 · outbound

This paper cites Reward is enough for convex mdps.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Reward is enough for convex mdps

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:00.965177Z

Source-reported events for the cited work

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Observation 70cf53ee-3fe7-443e-bb61-d43a2682383d · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Star: Bootstrapping reasoning with reasoning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.176430Z

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Observation 515c704c-2b96-4e6b-bef0-c23004832a7e · outbound

This paper cites Variational policy gradient method for reinforcement learning with general utilities.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Variational policy gradient method for reinforcement learning with general utilities

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.180627Z

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Observation c7d0718c-3cb6-4fc9-aea5-be94c0641cd8 · outbound

This paper cites Estimation and inference in distributional reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Estimation and inference in distributional reinforcement learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.184885Z

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Observation 9851e4c7-a1ef-4e60-8eaa-f776dfdf292e · outbound

This paper cites Beyond markovian: Reflective exploration via bayes-adaptive rl for llm reasoning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Beyond markovian: Reflective exploration via bayes-adaptive rl for llm reasoning

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:00.189171Z

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Observation 0534ec8d-deb2-4ca9-84d1-a6cf374ac558 · outbound

This paper cites Settling the sample complexity of online reinforcement learning.

Theoretical Foundations of $\max$@$k$ Reinforcement Learning Settling the sample complexity of online reinforcement learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:43:00.927032Z

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