Reinforcement Learning
1개 벤치마크 · 논문 23,845편 · 이 태스크의 논문 보기 →
Benchmarks
iris
Most implemented
Proximal Policy Optimization Algorithms
Continuous control with deep reinforcement learning
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Playing Atari with Deep Reinforcement Learning
Deep Reinforcement Learning with Double Q-learning
Papers
HiPLEX: Hierarchical Policy Factorization for Full Duplex Speech Language Models
As human--AI interactions become more conversational, full-duplex speech language models capable of natural real-time dialogue are growing in importance. Beyond generating appropriate responses, these models must coordin…
Reinforcement LearningOn KL-Regularized Policy Optimization
Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come from stale checkpoints, and the inference engine's probabilities differ…
Reinforcement LearningSherpa: Teaching LLMs to Teach Adaptively
Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it. Existing approaches to training LLMs as teachers rely on demonst…
Reinforcement LearningNeMo-DCR: Bit-Exact Delta-Compressed Refit for Scalable Agentic RL at Trillion-Parameter Scale
Agentic reinforcement learning (RL) disaggregates training from rollout, so each policy update must reach the rollout clusters before the next batch. Transferring a full 1T checkpoint for such weight synchronization (ref…
Reinforcement LearningTRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation, which motivates low-precision rollout for efficient RL training. Ho…
Reinforcement LearningBase Models Can Reason By Taking a Cue From Training Data
In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonstrate that fixing particular starting tok…
Reinforcement Learning