Knowledge Tree
reinforcement learning
Also known as: RL
synthesized from dimensionsReinforcement learning (RL) is a fundamental machine learning paradigm focused on optimizing action trajectories within complex, dynamic environments optimizes action trajectories. By enabling agents to manage uncertainty through interaction and feedback, RL facilitates the development of systems capable of solving multi-step tasks manages uncertainty in agents. As established in foundational literature such as the work of Sutton and Barto Sutton and Barto book, the field has evolved from a theoretical framework into a primary mechanism for training autonomous agents and aligning sophisticated models with human objectives.
In the context of large language models (LLMs), RL has become the standard method for alignment and the enhancement of reasoning capabilities standard for alignment. Recent implementations, such as the DeepSeek-R1 framework, demonstrate that RL can effectively incentivize reasoning by rewarding the generation of logical chains of thought DeepSeek-R1 incentivizes reasoning DeepSeek-R1 framework. This process is often supported by symbolic constraints or planning modules, as seen in research from Amazon and other institutions, which integrate symbolic plans to guide high-level RL instructions symbolic plans for RL Amazon lab's LLM-RL combo.
A significant advantage of RL over supervised fine-tuning is its superior generalization capability RL superiority in generalization Chu et al. evidence. This is attributed to the "generation-verification gap," where it is computationally easier for a model to learn to verify a correct solution than to generate one from scratch. Theoretical analyses suggest that RL updates models within distinct, low-curvature subspaces Zhu et al. proof, and that Reinforcement Learning from Human Feedback (RLHF) may outperform methods like Direct Preference Optimization (DPO) when the underlying policy is misspecified RLHF superior when policy misspecified.
Despite its efficacy, the application of RL is constrained by the quality of the reward signal. Dependence on these signals introduces risks such as reward hacking, where an agent optimizes for the reward metric rather than the intended behavior efficacy limited by rewards. Furthermore, there is an ongoing debate regarding whether RL truly instills novel reasoning capabilities or merely elicits latent abilities already present in the model from its pre-training phase debate on reasoning emergence. Alternative approaches, such as contrastive preference learning, are sometimes proposed to avoid the complexities and risks associated with traditional RL-based feedback.
Industrial and research applications of RL are diverse, ranging from ad auctions at Amazon Amazon Ads employs RL to adaptive loss mechanisms at Apple adaptive loss RL and specialized techniques like Generative Evaluator Tuning Generative Evaluator Tuning. As the field matures, it continues to serve as a critical pillar for AI safety and alignment, with ongoing scrutiny regarding the long-term implications of human feedback loops Lambert et al. paper.
Model Perspectives (2)
openrouter/x-ai/grok-4.1-fast definitive 88% confidence
Reinforcement learning (RL) is a machine learning paradigm for optimizing action trajectories in complex tasks and dynamic environments, often partitioning problems into subtasks or learning from rewards.optimizes action trajectories manages uncertainty in agents It serves as the standard method for aligning models with human values and enhancing reasoning in large language models (LLMs).standard for alignment Recent works like DeepSeek-R1 by DeepSeek-AI use RL to incentivize reasoning capabilities in LLMs.DeepSeek-R1 incentivizes reasoning Swamy et al. (2025) explain RL's generalization edge via the 'generation-verification gap,' where verifiers are easier to learn than generators.RL superiority in generalization Theoretical analyses, such as Azar et al. (2024), decompose RL performance gaps, showing RLHF outperforms DPO under policy misspecification.RLHF superior when policy misspecified RL combines with symbolic AI, as in Illanes et al.'s symbolic plans for high-level RL instructions.symbolic plans for RL Industrial applications include Amazon's Sponsored Products team using RL for ad auctions.Amazon Ads employs RL Limitations include dependence on reward signal quality and risks like reward hacking.efficacy limited by rewards Debates question if RL instills novel reasoning or elicits latent pre-training abilities.debate on reasoning emergence
openrouter/x-ai/grok-4.1-fast 88% confidence
Reinforcement learning (RL) emerges from the facts as a key machine learning paradigm with foundational texts like the 2018 book 'Reinforcement learning: An introduction' by Sutton and Barto book, and ongoing scrutiny of its history and risks with human feedback per Lambert et al. paper. In large language models (LLMs), RL drives reasoning improvements, as in DeepSeek-R1 framework where rewards coordinate experts with symbolic constraints (arXiv), Amazon lab's LLM-RL combo for planning (Amazon Science), and OpenAI's reinforcement of chain-of-thought traces (Zylos). RL excels in generalization over supervised fine-tuning, per Chu et al. evidence (arXiv), and updates models in distinct low-curvature subspaces according to Zhu et al. proof (arXiv). It forms a pillar of AI alignment through mechanistic analysis (arXiv) and defends multimodal LLMs against conflicts better than alternatives (GitHub). Specific applications include Generative Evaluator Tuning with KnowLLM rewards (arXiv), federated regret bounds in Fed-UCBVI algorithm (AISTATS; Samuel Tesfazgi et al.), neural symbolic RL for text games (Kimura et al., arXiv), and Apple's adaptive loss RL (Apple Machine Learning Research). Alternatives like contrastive preference learning avoid RL for human feedback (Hejna et al., ICLR 2024).
Facts (101)
Sources
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 27 facts
referenceThe paper 'Deepseek-r1: incentivizing reasoning capability in llms via reinforcement learning' (arXiv:2501.12948) is cited in the survey 'A Survey on the Theory and Mechanism of Large Language Models' regarding reasoning capabilities.
referenceThe paper 'SFT memorizes, RL generalizes: a comparative study of foundation model post-training' was published in the Proceedings of the 42nd International Conference on Machine Learning, Vol. 267, pp. 10818–10838.
claimZhao et al. (2025b) characterized Reinforcement Learning as an “echo chamber” that converges to a single dominant output format found in pre-training data, which suppresses diversity while enabling positive transfer from simple to complex tasks.
claimWhile researchers often prefer Adam over SGD in adversarial neural networks and reinforcement learning due to faster practical convergence, there is no definitive theoretical proof establishing that Adam is superior to SGD.
claimSwamy et al. (2025) attributed the superiority of Reinforcement Learning (RL) in generalization to the 'generation-verification gap,' arguing that in many reasoning tasks, learning a verifier is significantly easier than learning a generator.
claimFan et al. (2025) attribute the tendency of reasoning models to fall into redundant loops of self-doubt and hallucination to current Reinforcement Learning (RL) mechanisms that over-reward detailed Chain-of-Thought.
claimReinforcement learning on incorrect responses helps models identify and unlearn 'spurious correlations'—incorrect intermediate steps that lead to correct final answers—scaling synthetic dataset efficiency by eight-fold compared to standard positive-only fine-tuning.
referenceThe research paper 'ProRL: prolonged reinforcement learning expands reasoning boundaries in large language models' was published in the International Conference on Machine Learning, pp. 4051–4060, and cited in section 7.2.2 of the survey.
referenceThe paper 'CoT-space: a theoretical framework for internal slow-thinking via reinforcement learning' proposes a framework for internal reasoning (slow-thinking) in models using reinforcement learning.
claimAzar et al. (2024) theoretically decomposed the performance gap in Reinforcement Learning into exact optimization and finite-sample regimes, proving that Reinforcement Learning from Human Feedback (RLHF) is superior when the policy model is misspecified, whereas Direct Preference Optimization (DPO) excels when the reward model is misspecified.
claimReinforcement Learning (RL) is the standard method for aligning models with complex human values and enhancing reasoning capabilities.
measurementSetlur et al. (2024) found that in mathematical reasoning tasks, using reinforcement learning on a model's incorrect responses is twice as sample-efficient as fine-tuning on correct synthetic answers.
claimA central debate in the theoretical community concerns whether Reinforcement Learning (RL) truly instills new reasoning capabilities in Large Language Models or merely elicits latent abilities acquired during pre-training.
claimRecent research in Reinforcement Learning (RL) for model alignment focuses on dissecting the mechanisms of how RL alters model behavior, comparing optimization landscapes of different algorithms, and understanding the risks of reward hacking.
claimSetlur et al. (2025) prove that Verifier-Based methods, such as reinforcement learning or search, possess a distinct theoretical advantage over Verifier-Free methods like behavioral cloning.
claimYang et al. (2025b) identified a gradient anomaly in reinforcement learning training where low-probability tokens generate disproportionately large gradient magnitudes, which suppresses the learning of high-probability tokens.
claimThe two-stage reinforcement learning process utilizes a simpler reward model to narrow the search space, effectively guiding the policy toward a subset of optimal solutions that offline cloning cannot easily identify.
claimThe efficacy of Reinforcement Learning is fundamentally limited by the quality of the reward signal.
claimLiu et al. (2025d) demonstrated that with sufficient training duration and periodic policy resets, Reinforcement Learning can drive Large Language Models to explore novel strategies absent in the base model, thereby expanding the reasoning boundary.
claimThe research paper 'All roads lead to likelihood: the value of reinforcement learning in fine-tuning' (arXiv:2503.01067) analyzes the role and value of reinforcement learning in the fine-tuning process of large language models.
referenceThe paper 'Rlprompt: optimizing discrete text prompts with reinforcement learning' was published in the Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3369–3391.
claimShao et al. (2025) found that even weak or random reward signals can significantly improve mathematical reasoning in Large Language Models because Reinforcement Learning activates valid reasoning modes, such as code-based reasoning, already present in the pre-trained model.
claimYue et al. (2025) systematically evaluated RL with Verifiable Rewards (RLVR) and argued that while RL improves sampling efficiency, it does not introduce fundamentally new reasoning patterns, with performance ultimately bounded by the base model’s distribution.
claimThe academic community has established two primary theoretical pillars for AI alignment: the pursuit of mathematical safety guarantees and the mechanistic analysis of Reinforcement Learning (RL) dynamics.
referenceThe paper 'Detecting data contamination from reinforcement learning post-training for large language models' is an arXiv preprint, arXiv:2510.09259.
claimZhu et al. (2025c) proved that Reinforcement Learning updates occur in low-curvature subspaces orthogonal to the principal components updated by Supervised Fine-Tuning (SFT), suggesting that Reinforcement Learning operates in a distinct optimization regime that fine-tunes behavior without significantly altering primary feature representations.
claimChu et al. (2025) provided empirical evidence that Supervised Fine-Tuning (SFT) tends to memorize training data, leading to poor performance on out-of-distribution (OOD) tasks, whereas Reinforcement Learning (RL) demonstrates superior generalization capabilities.
A comprehensive overview on demand side energy management ... link.springer.com Mar 13, 2023 13 facts
referenceKim B-G, Zhang Y, Van Der Schaar M, and Lee J-W (2015) explored the use of reinforcement learning for dynamic pricing and energy consumption scheduling in smart grids.
referenceO’Neill et al. (2010) implemented residential demand response strategies using reinforcement learning techniques.
referenceWen et al. (2015) developed an optimal demand response strategy using device-based reinforcement learning, published in IEEE Transactions on Smart Grid.
claimThe three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning (Murphy 2012).
referenceKim B-G, Zhang Y, Van Der Schaar M, and Lee J-W (2015) explored dynamic pricing and energy consumption scheduling using reinforcement learning, published in IEEE Transactions on Smart Grid.
claimIn the context of energy management optimization, RL stands for Reinforcement learning.
referenceVázquez-Canteli and Nagy published the paper 'Reinforcement learning for demand response: a review of algorithms and modeling techniques' in Applied Energy in 2019.
claimReinforcement learning involves determining how agents should perform actions in an environment to maximize cumulative rewards, and Q-learning is commonly used at the Home Energy Management System (HEMS) level to optimize appliance scheduling using cost and user comfort as reward functions (O’Neill et al. 2010; Wen et al. 2015).
claimMurphy (2012) classifies the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning.
referenceO’Neill D, Levorato M, Goldsmith A, and Mitra U presented the paper 'Residential demand response using reinforcement learning' at the 2010 First IEEE International Conference on Smart Grid Communications.
referenceVázquez-Canteli and Nagy (2019) reviewed reinforcement learning algorithms and modeling techniques for demand response in the article 'Reinforcement learning for demand response: a review of algorithms and modeling techniques' published in Applied Energy.
claimReinforcement learning is defined as the task of determining how agents should perform actions in a given environment to maximize cumulative rewards.
referenceZhang X, Lu R, Jiang J, Hong SH, and Song WS published 'Testbed implementation of reinforcement learning-based demand response energy management system' in the journal Applied Energy in 2021.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 9 facts
claimNumerical experiments conducted by Yan Yang, Bin Gao, and Ya-xiang Yuan demonstrate that the hyper-gradient serves as an integration of exploitation and exploration in reinforcement learning.
claimEstimation of the Average Treatment Effect (ATE) is a core problem in causal inference with strong connections to Off-Policy Evaluation in Reinforcement Learning.
claimThe research by Aya Kayal, Sattar Vakili, Laura Toni, and Alberto Bernacchia derives new confidence intervals for kernel ridge regression specifically tailored to the reinforcement learning setting.
claimAbdullah Tokmak, Kiran Krishnan, Thomas Schön, and Dominik Baumann applied their safe Bayesian optimization algorithm to optimize reinforcement learning policies on physics simulators and a real inverted pendulum, demonstrating improved performance, safety, and scalability compared to state-of-the-art methods.
claimOff-policy evaluation (OPE) in reinforcement learning is the problem of estimating the expected long-term payoff of a target policy using experiences generated by a different, potentially unknown, behavior policy.
claimStochastic multi-armed bandits (MABs) are a fundamental reinforcement learning model used to study sequential decision-making in uncertain environments.
procedureDecision Points RL (DPRL) is an algorithm that restricts the set of state-action pairs or regions for continuous states considered for improvement to ensure high-confidence improvement in densely visited states, while utilizing data from sparsely visited states for trajectory-based value estimates.
claimZilong Deng, Simon Khan, and Shaofeng Zou study the sample complexity of risk-sensitive Reinforcement Learning with a generative model, specifically focusing on maximizing the Conditional Value at Risk (CVaR) with a risk tolerance level tau at each step, a problem they name Iterated CVaR.
measurementThe regret of the Federated Upper Confidence Bound Value Iteration algorithm (Fed-UCBVI) scales as Õ(√(H^3 |S| |A| T / M)), where |S| is the number of states, |A| is the number of actions, H is the episode length, M is the number of agents, and T is the number of episodes, with an additional small term accounting for agent heterogeneity.
A Survey of Incorporating Psychological Theories in LLMs - arXiv arxiv.org 8 facts
claimCao et al. (2024) introduced a method for enhancing reinforcement learning by utilizing dense rewards derived from a language model critic.
claimAdding reward variability in reinforcement learning may reduce premature convergence and improve alignment with human intent.
claimPsychological insights have historically influenced key Natural Language Processing (NLP) breakthroughs, specifically the cognitive underpinnings of attention mechanisms, reinforcement learning, and Theory of Mind-inspired social modeling.
referenceSriyash Poddar et al. introduced 'Personalizing reinforcement learning from human feedback with variational preference learning' in 2024, a method for personalizing reinforcement learning models.
referencePeter Dayan and Nathaniel D. Daw authored 'Decision theory, reinforcement learning, and the brain', published in the journal Cognitive, Affective, & Behavioral Neuroscience in 2008.
referenceNathan Lambert, Thomas Krendl Gilbert, and Tom Zick published 'The history and risks of reinforcement learning and human feedback' in 2023.
referenceJoey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, and Dorsa Sadigh proposed 'Contrastive preference learning' as a method for learning from human feedback without reinforcement learning in a 2024 paper presented at The Twelfth International Conference on Learning Representations.
claimGollapalli & Ng (2025) merged persuasive dialog acts with reinforcement learning to influence LLM collaborative settings.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 4 facts
referenceThe paper 'Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning' was published as an arXiv preprint in 2025.
procedureThe detect-understand-act (DUA) framework operates in three stages: the detect module uses computer vision to process unstructured data into symbolic representations; the understand component uses answer set programming (ASP) and inductive logic programming (ILP) to ensure decisions align with symbolic rules; and the act component uses pre-trained reinforcement learning policies to refine symbolic representations.
referenceThe detect-understand-act (DUA) framework is an example of the cooperative neuro-symbolic paradigm in reinforcement learning, where neural and symbolic components collaborate iteratively to solve tasks.
claimIn the DeepSeek-R1 framework, reinforcement learning rewards and symbolic constraints coordinate specialized experts, allowing for efficient resource utilization and adherence to reasoning rules.
Complexity and the Evolution of Consciousness | Biological Theory link.springer.com Sep 14, 2022 4 facts
perspectiveThe author of 'Complexity and the Evolution of Consciousness' suggests that 'Benthamite creatures' is a more appropriate term than 'Skinnerian creatures' (a term used by Daniel Dennett in 1995) for describing organisms capable of reinforcement learning, as it avoids externalist modes of thinking about the transition to animal agency.
referenceReinforcement learning capacities imply both sensitivity to rewards and the updating of behavioral dispositions based on the consequences of earlier behavior, according to Spurrett (2020).
referenceReinforcement learning abilities are ubiquitous in the animal branch of life, including in cephalopods, crustaceans, and insects, as noted by Perry et al. (2013).
claimThe author of 'Complexity and the Evolution of Consciousness' argues that the ability for reinforcement learning is more complex than typically acknowledged, noting that cyberneticists struggle to design robots that achieve the basic successes of simple animal life.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 3 facts
referenceLeón Illanes, Xi Yan, Rodrigo Toro Icarte, and Sheila A McIlraith authored the paper 'Symbolic plans as high-level instructions for reinforcement learning', published in the Proceedings of the International Conference on Automated Planning and Scheduling, Vol. 30, pp. 540–550, in 2020.
referenceBello and Malle (2023) proposed applying the Belief-Desire-Intention (BDI) model combined with reinforcement learning to simulate how subjects internalize norms and make decisions consistent with human moral judgment, offering a potential path for modeling complex moral behavior in Neuro-Symbolic AI.
referenceKimura et al. (2021) proposed a neural symbolic framework designed to solve reinforcement learning problems in text-based games.
EdinburghNLP/awesome-hallucination-detection - GitHub github.com 3 facts
procedureFactuality-aware Step-wise Policy Optimization (FSPO) is a reinforcement learning fine-tuning algorithm that incorporates explicit factuality verification at each reasoning step and dynamically adjusts token-level advantage values to maintain factual correctness.
referenceRL4HS is a reinforcement-learning framework for span-level hallucination detection that couples chain-of-thought reasoning with span-level rewards, utilizing Group Relative Policy Optimization (GRPO) and Class-Aware Policy Optimization (CAPO) to address reward imbalance between hallucinated and non-hallucinated spans.
claimReinforcement learning provides the most robust defense against modality conflict in Multimodal Large Language Models by training the model to prioritize visual evidence over misleading textual cues, compared to prompt engineering and supervised fine-tuning.
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 2 facts
claimExisting agent technologies partition complex tasks into manageable subtasks by either harnessing symbolic AI to systematically explore potential actions or employing reinforcement learning to optimize action trajectories.
referenceRichard S. Sutton and Andrew G. Barto authored the book 'Reinforcement learning: An introduction', published by MIT Press in 2018.
Papers - Dr Vaishak Belle vaishakbelle.github.io 2 facts
referenceThe paper 'Deep Inductive Logic Programming meets Reinforcement Learning' by A. Bueff and V. Belle was published in the ICLP proceedings in 2023.
referenceThe paper 'Logic + Reinforcement Learning + Deep Learning: A Survey' by A. Bueff and V. Belle was published in the ICAART proceedings in 2023.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 2 facts
claimExisting agent technologies for complex, multi-step goals either harness symbolic AI to systematically explore potential action spaces or employ reinforcement learning to optimize action trajectories by partitioning tasks into subtasks.
claimFoundational techniques for autonomous agent design originate from classic AI approaches, including Probabilistic Graphical Models, Reinforcement Learning, and Multi-Agent Systems, which manage uncertainty, learn optimal behaviors in dynamic environments, and enable agents to interact and share information efficiently.
Detecting hallucinations with LLM-as-a-judge: Prompt ... - Datadog datadoghq.com Aug 25, 2025 2 facts
claimPrompts are used to augment labeled data with reasoning chains for supervised fine-tuning (SFT) or in SFT initialization steps before reinforcement learning (RL).
claimLarge-scale reinforcement learning in Large Language Models elicits reasoning behaviors such as hypothesis generation and self-criticism as emergent properties.
Construction of intelligent decision support systems through ... - Nature nature.com Oct 10, 2025 2 facts
claimThe knowledge orchestration engine within the Integrated Knowledge-Enhanced Decision Support framework uses reinforcement learning to improve pathway selection based on observed outcomes.
claimThe Dynamic Knowledge Orchestration Engine utilizes reinforcement learning to continuously improve the selection of pathways based on task outcomes.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 2 facts
referenceTyagi, Sarkar, and Gaur (2023) investigated leveraging knowledge and reinforcement learning to enhance the reliability of language models.
procedureGenerative Evaluator Tuning is a method that uses reinforcement learning to train e-LLMs by combining traditional training with rewards from KnowLLMs, which act as extra guidelines. If an e-LLM's output is logically incorrect according to KnowLLM or fails to meet specific criteria, it receives negative rewards, even if the output is similar to the ground truth based on similarity scores.
Knowledge graphs - Amazon Science amazon.science 2 facts
procedureThe responsibilities of an Applied Scientist on the Sponsored Products and Brands Off-Search team include designing and developing solutions using GenAI, deep learning, multi-objective optimization, and reinforcement learning to improve ad retrieval, auctions, and whole-page relevance.
claimThe Amazon research lab combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments.
Unknown source 1 fact
claimThe authors of the paper 'possible evolutionary function of phenomenal conscious experience' propose that pain contributes to evolutionary fitness through an actor-critic functional architecture for reinforcement learning.
Zero-knowledge LLM hallucination detection and mitigation through ... amazon.science 1 fact
claimThe Sponsored Products and Brands (SPB) team at Amazon Ads develops solutions involving generative AI, deep learning, multi-objective optimization, and reinforcement learning to improve ad retrieval, auctions, and whole-page relevance.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
referenceExisting survey papers on neuro-symbolic AI generally focus on broad overviews or specific applications, including cybersecurity, military operations, reinforcement learning, knowledge graph reasoning, and validation and verification.
Neuro-Symbolic AI: Explainability, Challenges & Future Trends linkedin.com Dec 15, 2025 1 fact
referenceThe research paper '1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities' by researchers at CMU and Google Research (arXiv:2503.14858) demonstrates that structural depth, rather than data volume or reward design, is a key factor in neural network performance for reinforcement learning.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
claimCausal filtering, knowledge provenance tracing, and reinforcement learning are potential methods to suppress self-reinforcing loops and error propagation in Knowledge Graph and Large Language Model systems.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 1 fact
claimXiong W, Hoang T, Wang WY published the paper 'Deep path: a reinforcement learning method for knowledge graph reasoning' as an arXiv preprint in 2017.
LLM Hallucinations: Causes, Consequences, Prevention - LLMs llmmodels.org May 10, 2024 1 fact
claimReinforcement learning is an emerging technique to solve LLM hallucinations by training large language models using a reward function that penalizes hallucinated outputs.
Quantum Approaches to Consciousness plato.stanford.edu Nov 30, 2004 1 fact
referenceThe quantum approach to agency proposed by Briegel and Müller is based on 'projective simulation,' a quantum algorithm for reinforcement learning in neural networks developed by Paparo et al. (2012), which is considered a variant of quantum machine learning as defined by Wittek (2014).
A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org Jan 6, 2026 1 fact
referenceDeepSeek-AI published the DeepSeek-R1 technical report in 2025, detailing the use of reinforcement learning to incentivize reasoning capabilities in large language models.
A Comprehensive Review on Residential Demand Side Management ideas.repec.org 1 fact
referenceOmar Al-Ani and Sanjoy Das published 'Reinforcement Learning: Theory and Applications in HEMS' in the journal Energies in September 2022.
Demand side management using optimization strategies for efficient ... journals.plos.org Mar 21, 2024 1 fact
referenceSharma R. and Gopal M. proposed synergizing reinforcement learning and game theory as a new direction for control systems in a 2010 paper in Applied Soft Computing.
Beyond Missile Deterrence: The Rise of Algorithmic Superiority trendsresearch.org Mar 16, 2026 1 fact
claimReinforcement-learning techniques allow attackers to refine their strategies within defended and changing digital environments.
LLM Hallucination Detection and Mitigation: State of the Art in 2026 zylos.ai Jan 27, 2026 1 fact
claimOpenAI's 2026 research on reasoning models demonstrates that naturally understandable chain-of-thought reasoning traces are reinforced through reinforcement learning, and that simple prompted GPT-4o models can effectively monitor for reward hacking in frontier reasoning models like o1 and o3-mini successors.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 1 fact
claimDeepSeek-R1 is a reasoning-optimized LLM that employs large-scale reinforcement learning on scientific and mathematical tasks to enhance logical consistency and reduce confabulation.
Evaluating Evaluation Metrics — The Mirage of Hallucination ... machinelearning.apple.com 1 fact
procedureIn the paper 'Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment', the authors propose a sample-efficient reinforcement learning approach for adapting the loss function dynamically during training to directly optimize the evaluation metric.
Do LLMs Build World Representations? Probing Through the Lens of... openreview.net Sep 25, 2024 1 fact
claimThe authors of "Do LLMs Build World Representations? Probing Through the Lens ..." propose a framework for probing world representations in Large Language Models using state abstraction theory from reinforcement learning, which distinguishes between general abstractions that facilitate predicting future states and goal-oriented abstractions that guide actions to accomplish tasks.