Papers Reinforcement Learning (RL)
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Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, as well ascontrol switching issues between different states such as up-…
Reinforcement Learning (RL)Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this …
EEGMuJoCoreinforcement-learningReinforcement Learning+1VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks
Recent advances in reinforcement learning (RL) have led to substantial improvements in the mathematical reasoning abilities of large language models (LLMs), as measured by standard benchmarks. However, these gains often …
MathMathematical ReasoningReinforcement Learning (RL)QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Reinforcement learning (RL) has become a key component in training large language reasoning models (LLMs). However, recent studies questions its effectiveness in improving multi-step reasoning-particularly on hard proble…
MathReinforcement Learning (RL)Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intelligence. The recent success of reasoning …
Language ModelingLanguage ModellingLarge Language ModelReinforcement Learning (RL)Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Behavior Cloning (BC) on curated (or filtered) data is the predominant paradigm for supervised fine-tuning (SFT) of large language models; as well as for imitation learning of control policies. Here, we draw on a connect…
continuous-controlContinuous ControlImitation LearningReinforcement Learning (RL)From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) aims to learn effective policies from a static dataset without requiring further agent-environment interactions. However, its practical adoption is often hindered by the need for expli…
D4RLOffline RLreinforcement-learningReinforcement Learning+1Kevin: Multi-Turn RL for Generating CUDA Kernels
Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiab…
GPUReinforcement Learning (RL)Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models
Game design hinges on understanding how static rules and content translate into dynamic player behavior - something modern generative systems that inspect only a game's code or assets struggle to capture. We present an a…
Game DesignReinforcement Learning (RL)Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
Recent advancements in reasoning-focused language models such as OpenAI's O1 and DeepSeek-R1 have shown that scaling test-time computation-through chain-of-thought reasoning and iterative exploration-can yield substantia…
Code GenerationMathreinforcement-learningReinforcement Learning+2Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction
Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophistic…
Change Point DetectionReinforcement Learning (RL)Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Three core tenets of reinforcement learning (RL)--concerning the definition of agency, the objective of learning, and the scope of the reward hypothesis--have been highlighted as key targets for conceptual revision, with…
Reinforcement Learning (RL)Exploring the robustness of TractOracle methods in RL-based tractography
Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) has emerged as a promising framework for …
Diffusion MRIreinforcement-learningReinforcement LearningReinforcement Learning (RL)Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing exercise recommendation approaches simulat…
Knowledge TracingMathQ-LearningReinforcement Learning (RL)Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning
Training neural networks with reinforcement learning (RL) typically relies on backpropagation (BP), necessitating storage of activations from the forward pass for subsequent backward updates. Furthermore, backpropagating…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
Vision-language models (VLMs) are increasingly applied to identify unsafe or inappropriate images due to their internal ethical standards and powerful reasoning abilities. However, it is still unclear whether they can re…
Reinforcement Learning (RL)High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Scaling reinforcement learning (RL) workloads often requires distributing environment simulation across compute clusters. Existing frameworks entangle simulation, learning logic, and orchestration into monolithic systems…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
The reasoning capabilities of large language models (LLMs) have been a longstanding focus of research. Recent works have further enhanced these capabilities using reinforcement learning (RL), with many new methods claimi…
MathMathematical ReasoningMemorizationReinforcement Learning (RL)Deep Reinforcement Learning with Gradient Eligibility Traces
Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are …
Deep Reinforcement LearningMuJoCoreinforcement-learningReinforcement Learning+1A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
Enhancing the mathematical reasoning of Large Language Models (LLMs) is a pivotal challenge in advancing AI capabilities. While Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are the dominant training parad…
MathMathematical ReasoningReinforcement Learning (RL)