{"total":65,"rows":[{"id":"2403.00514","title":"Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning","created":"2024-03-01","cited_by_count":50,"area":"deeprl"},{"id":"2405.00662","title":"No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO","created":"2024-05-01","cited_by_count":27,"area":"deeprl"},{"id":"2509.20758","title":"SFT Doesn't Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs","created":"2025-09-25","cited_by_count":24,"area":null},{"id":"2506.00592","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","created":"2025-05-31","cited_by_count":22,"area":"deeprl"},{"id":"2411.04034","title":"Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset","created":"2024-11-06","cited_by_count":17,"area":"deeprl"},{"id":"2507.12224","title":"Optimizers Qualitatively Alter Solutions And We Should Leverage This","created":"2025-07-16","cited_by_count":17,"area":null},{"id":"2505.24061","title":"Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning","created":"2025-05-29","cited_by_count":14,"area":"deeprl"},{"id":"2508.12531","title":"Rethinking Safety in LLM Fine-tuning: An Optimization Perspective","created":"2025-08-17","cited_by_count":14,"area":"lr"},{"id":"2510.13704","title":"Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents","created":"2025-10-15","cited_by_count":12,"area":"deeprl"},{"id":"2404.02113","title":"Position: Lifetime tuning is incompatible with continual reinforcement learning","created":"2024-04-02","cited_by_count":11,"area":"deeprl"},{"id":"2507.02712","title":"A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control","created":"2025-07-03","cited_by_count":9,"area":"deeprl"},{"id":"2602.08040","title":"FIRE: Frobenius-Isometry Reinitialization for Balancing the Stability-Plasticity Tradeoff","created":"2026-02-08","cited_by_count":7,"area":null},{"id":"2508.00212","title":"Reinitializing weights vs units for maintaining plasticity in neural networks","created":"2025-07-31","cited_by_count":6,"area":null},{"id":"2509.22562","title":"Activation Function Design Sustains Plasticity in Continual Learning","created":"2025-09-26","cited_by_count":6,"area":null},{"id":"2505.13811","title":"Context-Free Synthetic Data Mitigates Forgetting","created":"2025-05-20","cited_by_count":5,"area":"synth"},{"id":"2510.00304","title":"Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity","created":"2025-09-30","cited_by_count":5,"area":null},{"id":"2502.21147","title":"Same accuracy, twice as fast: continuous training surpasses retraining from scratch","created":"2025-02-28","cited_by_count":4,"area":null},{"id":"2503.17500","title":"Variance Control via Weight Rescaling in LLM Pre-training","created":"2025-03-21","cited_by_count":4,"area":null},{"id":"2503.20018","title":"Experience Replay Addresses Loss of Plasticity in Continual Learning","created":"2025-03-25","cited_by_count":4,"area":null},{"id":"2506.01016","title":"Optimistic critics can empower small actors","created":"2025-06-01","cited_by_count":4,"area":"deeprl"},{"id":"2602.01460","title":"Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator","created":"2026-02-01","cited_by_count":4,"area":"deeprl"},{"id":"2602.11137","title":"Weight Decay Improves Language Model Plasticity","created":"2026-02-11","cited_by_count":4,"area":null},{"id":"2605.09044","title":"Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective","created":"2026-05-09","cited_by_count":4,"area":null},{"id":"2605.10973","title":"Rotation-Preserving Supervised Fine-Tuning","created":"2026-05-08","cited_by_count":3,"area":null},{"id":"2412.14865","title":"Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning","created":"2024-12-19","cited_by_count":2,"area":"offlinerl"},{"id":"2412.18790","title":"Torque-Aware Momentum","created":"2024-12-25","cited_by_count":2,"area":null},{"id":"2503.01468","title":"Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization","created":"2025-03-03","cited_by_count":2,"area":"deeprl"},{"id":"2504.14677","title":"Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning","created":"2025-04-20","cited_by_count":2,"area":null},{"id":"2504.17490","title":"Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning","created":"2025-04-24","cited_by_count":2,"area":"deeprl"},{"id":"2505.09486","title":"Preserving Plasticity in Continual Learning with Adaptive Linearity Injection","created":"2025-05-14","cited_by_count":2,"area":null},{"id":"2506.02883","title":"A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks","created":"2025-06-03","cited_by_count":2,"area":"offlinerl"},{"id":"2602.09234","title":"Do Neural Networks Lose Plasticity in a Gradually Changing World?","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2505.01584","title":"Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming","created":"2025-05-02","cited_by_count":1,"area":"deeprl"},{"id":"2506.12389","title":"Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity","created":"2025-06-14","cited_by_count":1,"area":null},{"id":"2508.08052","title":"On Understanding of the Dynamics of Model Capacity in Continual Learning","created":"2025-08-11","cited_by_count":1,"area":null},{"id":"2509.03539","title":"An exact multiple-time-step variational formulation for the committor and the transition rate","created":"2025-08-29","cited_by_count":1,"area":null},{"id":"2511.17228","title":"Intrinsic preservation of plasticity in continual quantum learning","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2604.15414","title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","created":"2026-04-16","cited_by_count":1,"area":"deeprl"},{"id":"2604.17578","title":"Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2604.27063","title":"Learning to Forget: Continual Learning with Adaptive Weight Decay","created":"2026-04-29","cited_by_count":1,"area":null},{"id":"2606.09762","title":"Preserving Plasticity in Continual Learning via Dynamical Isometry","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2606.18487","title":"SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR","created":"2026-06-16","cited_by_count":1,"area":null},{"id":"2606.24752","title":"Can Scale Save Us From Plasticity Loss in Large Language Models?","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2608.18078","title":"Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions","created":"2026-05-29","cited_by_count":1,"area":"lr"},{"id":"2504.08000","title":"Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning","created":"2025-04-09","cited_by_count":0,"area":"deeprl"},{"id":"2506.10489","title":"Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2602.03846","title":"PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.09720","title":"Continual Learning for non-stationary regression via Memory-Efficient Replay","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.21173","title":"Rethinking Plasticity in Deep Reinforcement Learning","created":"2026-03-22","cited_by_count":0,"area":"deeprl"},{"id":"2604.01339","title":"Regularizing Attention Scores with Bootstrapping","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2605.01131","title":"Forager: a lightweight testbed for continual learning with partial observability in RL","created":"2026-05-01","cited_by_count":0,"area":"deeprl"},{"id":"2605.06834","title":"Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.15435","title":"On the Stability of Growth in Structural Plasticity: Forward-Active yet Backward-Starved","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.00284","title":"Specializing Without Forgetting: Analyzing Knowledge Preservation in Multilingual Model Adaptation","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.11797","title":"Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Reinforcement Learning","created":"2026-06-10","cited_by_count":0,"area":"deeprl"},{"id":"2607.13432","title":"Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2607.17922","title":"PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2607.24320","title":"Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform","created":"2026-07-27","cited_by_count":0,"area":"deeprl"},{"id":"2607.24996","title":"Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning","created":"2026-07-27","cited_by_count":0,"area":"deeprl"},{"id":"2608.04358","title":"NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.12874","title":"Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.18319","title":"SingularClip: Preventing Spectral Collapse to Maintain Plasticity in Continual and Reinforcement Learning","created":"2026-08-18","cited_by_count":0,"area":"deeprl"},{"id":"2608.29513","title":"On the Plasticity Collapse in Continual Machine Unlearning","created":"2026-08-30","cited_by_count":0,"area":"safety"},{"id":"2609.32819","title":"Rank Collapse Is Recoverable, Growing $|Q|$ Is Not: Out-of-Sample Early Warning for Value Divergence in High-UTD Soft Actor-Critic","created":"2026-09-26","cited_by_count":0,"area":"deeprl"},{"id":"2609.33127","title":"Policy Plasticity Matters in Offline-to-Online Reinforcement Learning: Refitting Offline Policies for Online Adaptation","created":"2026-09-27","cited_by_count":0,"area":"offlinerl"}],"next_cursor":null}