ICLR2024，强化学习领域约301篇Accept论文汇总整理，附原文与交流群

ICLR2024强化学习方面接受论文总共约301篇，其中oral文章11篇，spotlight文章59篇，Poster文章231篇，具体如下(原始论文pdf和交流群见文末，点击"阅读原文"查看)

Accept-Oral

[1]. Predictive auxiliary objectives in deep RL mimic learning in the brain [2]. Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning [3]. Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning [4]. SWE-bench: Can Language Models Resolve Real-world Github Issues? [5]. MetaGPT: Meta Programming for Multi-Agent Collaborative Framework [6]. METRA: Scalable Unsupervised RL with Metric-Aware Abstraction [7]. Mastering Memory Tasks with World Models [8]. Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. [9]. Learning Interactive Real-World Simulators [10]. Robust agents learn causal world models [11]. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

Accept-Spotlight [1]. Generalized Policy Iteration using Tensor Approximation for Hybrid Control [2]. A Theoretical Explanation of Deep RL Performance in Stochastic Environments [3]. A Benchmark on Robust Semi-Supervised Learning in Open Environments [4]. Generative Adversarial Inverse Multiagent Learning [5]. AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents [6]. Confronting Reward Model Overoptimization with Constrained RLHF [7]. Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling [8]. Harnessing Density Ratios for Online Reinforcement Learning [9]. Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning [10]. Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community [11]. Improving Offline RL by Blending Heuristics [12]. Tool-Augmented Reward Modeling [13]. Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning [14]. Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback [15]. Dual RL: Unification and New Methods for Reinforcement and Imitation Learning [16]. Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data [17]. Safe RLHF: Safe Reinforcement Learning from Human Feedback [18]. Cross$Q$: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity [19]. Blending Imitation and Reinforcement Learning for Robust Policy Improvement [20]. On the Role of General Function Approximation in Offline Reinforcement Learning [21]. Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies [22]. Massively Scalable Inverse Reinforcement Learning for Route Optimization [23]. Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation [24]. Towards Principled Representation Learning from Videos for Reinforcement Learning [25]. TorchRL: A data-driven decision-making library for PyTorch [26]. Towards Robust Offline Reinforcement Learning under Diverse Data Corruption [27]. DyST: Towards Dynamic Neural Scene Representations on Real-World Videos [28]. Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control [29]. Maximum Entropy Heterogeneous-Agent Reinforcement Learning [30]. Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics [31]. Text2Reward: Dense Reward Generation with Language Models for Reinforcement Learning [32]. Submodular Reinforcement Learning [33]. Query-Policy Misalignment in Preference-Based Reinforcement Learning [34]. Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies [35]. Provable Offline Preference-Based Reinforcement Learning [36]. Provable Reward-Agnostic Preference-Based Reinforcement Learning [37]. Entity-Centric Reinforcement Learning for Object Manipulation from Pixels [38]. Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm [39]. Addressing Signal Delay in Deep Reinforcement Learning [40]. DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization [41]. RealChat-1M: A Large-Scale Real-World LLM Conversation Dataset [42]. EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models [43]. SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series [44]. Quasi-Monte Carlo for 3D Sliced Wasserstein [45]. Cascading Reinforcement Learning [46]. Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning [47]. Efficient Distributed Training with Full Communication-Computation Overlap [48]. PTaRL: Prototype-based Tabular Representation Learning via Space Calibration [49]. $\mathcal{B}$-Coder: On Value-Based Deep Reinforcement Learning for Program Synthesis [50]. Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings [51]. Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization [52]. Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy [53]. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [54]. SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution [55]. BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation [56]. Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision [57]. TD-MPC2: Scalable, Robust World Models for Continuous Control [58]. Adaptive Rational Activations to Boost Deep Reinforcement Learning [59]. Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula

Accept-poster [1]. Locality Sensitive Sparse Encoding for Learning World Models Online [2]. Demonstration-Regularized RL [3]. KoLA: Carefully Benchmarking World Knowledge of Large Language Models [4]. On Representation Complexity of Model-based and Model-free Reinforcement Learning [5]. RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems [6]. Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning [7]. NP-GL: Extending Power of Nature from Binary Problems to Real-World Graph Learning [8]. Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning [9]. Improving Language Models with Advantage-based Offline Policy Gradients [10]. Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking [11]. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization [12]. Large Language Models as Automated Aligners for benchmarking Vision-Language Models [13]. Reverse Diffusion Monte Carlo [14]. PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning [15]. Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets [16]. Training Diffusion Models with Reinforcement Learning [17]. Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning [18]. Federated Q-Learning: Linear Regret Speedup with Low Communication Cost [19]. The Trickle-down Impact of Reward Inconsistency on RLHF [20]. Maximum Entropy Model Correction in Reinforcement Learning [21]. Simple Hierarchical Planning with Diffusion [22]. Regularized Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity [23]. Curriculum reinforcement learning for quantum architecture search under hardware errors [24]. Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits [25]. Directly Fine-Tuning Diffusion Models on Differentiable Rewards [26]. Tree Search-Based Policy Optimization under Stochastic Execution Delay [27]. Offline RL with Observation Histories: Analyzing and Improving Sample Complexity [28]. Understanding Hidden Context in Preference Learning: Consequences for RLHF [29]. Eureka: Human-Level Reward Design via Coding Large Language Models [30]. Active Retrosynthetic Planning Aware of Route Quality [31]. Fiber Monte Carlo [32]. Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization [33]. Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes [34]. Follow-the-Perturbed-Leader for Adversarial Bandits: Heavy Tails, Robustness, and Privacy [35]. ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models [36]. Score Models for Offline Goal-Conditioned Reinforcement Learning [37]. A Policy Gradient Method for Confounded POMDPs [38]. Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning [39]. Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning [40]. Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning [41]. Hindsight PRIORs for Reward Learning from Human Preferences [42]. Reward Model Ensembles Help Mitigate Overoptimization [43]. Feasibility-Guided Safe Offline Reinforcement Learning [44]. Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning [45]. Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation [46]. PAE: Reinforcement Learning from External Knowledge for Efficient Exploration [47]. Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML [48]. Identifying Policy Gradient Subspaces [49]. Contextual Bandits with Online Neural Regression [50]. PARL: A Unified Framework for Policy Alignment in Reinforcement Learning [51]. SafeDreamer: Safe Reinforcement Learning with World Models [52]. MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation [53]. GnnX-Bench: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking [54]. Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models [55]. Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback [56]. Goodhart's Law in Reinforcement Learning [57]. Score Regularized Policy Optimization through Diffusion Behavior [58]. Making RL with Preference-based Feedback Efficient via Randomization [59]. Adaptive Regret for Bandits Made Possible: Two Queries Suffice [60]. Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation [61]. Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping [62]. Demystifying Linear MDPs and Novel Dynamics Aggregation Framework [63]. PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization [64]. Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds [65]. Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning [66]. Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning [67]. Privileged Sensing Scaffolds Reinforcement Learning [68]. Learning Planning Abstractions from Language [69]. Tailoring Self-Rationalizers with Multi-Reward Distillation [70]. Building Cooperative Embodied Agents Modularly with Large Language Models [71]. A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning [72]. CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning [73]. Let Models Speak Ciphers: Multiagent Debate through Embeddings [74]. Learning interpretable control inputs and dynamics underlying animal locomotion [75]. Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data? [76]. Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX [77]. Searching for High-Value Molecules Using Reinforcement Learning and Transformers [78]. Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning [79]. Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations [80]. Privately Aligning Language Models with Reinforcement Learning [81]. On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning [82]. S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic [83]. Robust Model-Based Optimization for Challenging Fitness Landscapes [84]. Replay across Experiments: A Natural Extension of Off-Policy RL [85]. BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks [86]. Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning [87]. Time-Efficient Reinforcement Learning with Stochastic Stateful Policies [88]. Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning [89]. Incentivized Truthful Communication for Federated Bandits [90]. Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [91]. On Trajectory Augmentations for Off-Policy Evaluation [92]. Understanding the Effects of RLHF on LLM Generalisation and Diversity [93]. Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods [94]. Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding [95]. Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning [96]. GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks [97]. Incentive-Aware Federated Learning with Training-Time Model Rewards [98]. Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization [99]. Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity [100]. Off-Policy Primal-Dual Safe Reinforcement Learning [101]. STARC: A General Framework For Quantifying Differences Between Reward Functions [102]. GAIA: a benchmark for General AI Assistants [103]. Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning [104]. Discovering Temporally-Aware Reinforcement Learning Algorithms [105]. Revisiting Data Augmentation in Deep Reinforcement Learning [106]. Reward-Free Curricula for Training Robust World Models [107]. Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo [108]. CPPO: Continual Learning for Reinforcement Learning with Human Feedback [109]. Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations [110]. Bandits with Replenishable Knapsacks: the Best of both Worlds [111]. A Study of Generalization in Offline Reinforcement Learning [112]. Diverse Projection Ensembles for Distributional Reinforcement Learning [113]. MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations [114]. RLIF: Interactive Imitation Learning as Reinforcement Learning [115]. Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World [116]. Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization [117]. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [118]. EasyTPP: Towards Open Benchmarking Temporal Point Processes [119]. Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization [120]. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [121]. EasyTPP: Towards Open Benchmarking Temporal Point Processes [122]. Combinatorial Bandits for Maximum Value Reward Function under Value-Index Feedback [123]. Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic [124]. Video Language Planning [125]. Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining [126]. Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks [127]. Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning [128]. Diffusion Models for Multi-Task Generative Modeling [129]. Neural Active Learning Beyond Bandits [130]. Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages [131]. Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation [132]. Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees [133]. SALMON: Self-Alignment with Principle-Following Reward Models [134]. Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models [135]. SemiReward: A General Reward Model for Semi-supervised Learning [136]. Horizon-Free Regret for Linear Markov Decision Processes [137]. On Differentially Private Federated Linear Contextual Bandits [138]. Neural Neighborhood Search for Multi-agent Path Finding [139]. Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates [140]. Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks [141]. The Update Equivalence Framework for Decision-Time Planning [142]. Learning Reusable Dense Rewards for Multi-Stage Tasks [143]. Time Fairness in Online Knapsack Problems [144]. On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning [145]. RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment [146]. Reasoning with Latent Diffusion in Offline Reinforcement Learning [147]. Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time [148]. Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations [149]. SmartPlay : A Benchmark for LLMs as Intelligent Agents [150]. SOHES: Self-supervised Open-world Hierarchical Entity Segmentation [151]. Robust NAS benchmark under adversarial training: assessment, theory, and beyond [152]. SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos [153]. DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes [154]. Reward Design for Justifiable Sequential Decision-Making [155]. Fast Value Tracking for Deep Reinforcement Learning [156]. MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning [157]. Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [158]. LOQA: Learning with Opponent Q-Learning Awareness [159]. Intelligent Switching for Reset-Free RL [160]. On the Limitations of Temperature Scaling for Distributions with Overlaps [161]. True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning [162]. Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning [163]. Who to imitate: Imitating desired behavior from divserse multi-agent datasets [164]. SweetDreamer: Aligning Geometric Priors in 2D diffusion for Consistent Text-to-3D [165]. Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback [166]. Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning [167]. Learning Multi-Agent Communication from Graph Modeling Perspective [168]. Efficient Multi-agent Reinforcement Learning by Planning [169]. Sample-Efficient Multi-Agent RL: An Optimization Perspective [170]. CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery [171]. SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores [172]. Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks [173]. Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control [174]. Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion [175]. Parameter-Efficient Multi-Task Model Fusion with Partial Linearizeation [176]. Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs [177]. Multi-task Learning with 3D-Aware Regularization [178]. DMBP: Diffusion model based predictor for robust offline reinforcement learning against state observation perturbations [179]. Alignment as Reward-Guided Search [180]. Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts [181]. Retro-fallback: retrosynthetic planning in an uncertain world [182]. Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment [183]. AdaMerging: Adaptive Model Merging for Multi-Task Learning [184]. MetaTool Benchmark: Deciding Whether to Use Tools and Which to Use [185]. AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [186]. Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures [187]. LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents [188]. Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization [189]. Threshold-Consistent Margin Loss for Open-World Deep Metric Learning [190]. Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL [191]. Learning Multi-Agent Communication with Contrastive Learning [192]. Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. [193]. On Stationary Point Convergence of PPO-Clip [194]. Provably Efficient CVaR RL in Low-rank MDPs [195]. COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL [196]. Transport meets Variational Inference: Controlled Monte Carlo Diffusions [197]. In-context Exploration-Exploitation for Reinforcement Learning [198]. The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World [199]. TASK PLANNING FOR VISUAL ROOM REARRANGEMENT UNDER PARTIAL OBSERVABILITY [200]. Optimal Sample Complexity for Average Reward Markov Decision Processes [201]. DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing [202]. Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis [203]. Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform [204]. Combining Spatial and Temporal Abstraction in Planning for Better Generalization [205]. Decision Transformer is a Robust Contender for Offline Reinforcement Learning [206]. ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [207]. Bridging State and History Representations: Understanding Self-Predictive RL [208]. InstructDET: Diversifying Referring Object Detection with Generalized Instructions [209]. Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling [210]. GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING [211]. Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios [212]. VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks [213]. Grounding Multimodal Large Language Models to the World [214]. VFLAIR: A Research Library and Benchmark for Vertical Federated Learning [215]. Stylized Offline Reinforcement Learning: Extracting Diverse High-Quality Behaviors from Heterogeneous Datasets [216]. Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight [217]. Pre-training with Synthetic Data Helps Offline Reinforcement Learning [218]. AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [219]. Efficient Planning with Latent Diffusion [220]. A Benchmark Study on Calibration [221]. Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning [222]. Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [223]. Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification [224]. Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game [225]. AutoVP: An Automated Visual Prompting Framework and Benchmark [226]. AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval [227]. REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes [228]. Language Model Self-improvement by Reinforcement Learning Contemplation [229]. Towards Offline Opponent Modeling with In-context Learning [230]. Early Stopping Against Label Noise Without Validation Data [231]. Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited