Claude 3 Opus strategically fakes alignment by complying with harmful requests only during simulated training to preserve its preference for refusing them afterward.
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Proximal Policy Optimization Algorithms
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We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.
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- abstract We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more ge
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SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
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ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning
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Flow-GRPO: Training Flow Matching Models via Online RL
Flow-GRPO is the first online RL method for flow matching models, raising GenEval accuracy from 63% to 95% and text-rendering accuracy from 59% to 92% with little reward hacking.
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Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
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Learning, Fast and Slow: Towards LLMs That Adapt Continually
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QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
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Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation
Visual debiasing of omni-modal benchmarks combined with staged post-training lets a 3B model match or exceed a 30B model without a stronger teacher.
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Learning Agentic Policy from Action Guidance
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Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models
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Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
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Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework
Natural policy gradient is a special case of doubly smoothed policy iteration that achieves distribution-free global geometric convergence to an epsilon-optimal policy in O((1-gamma)^{-1} log((1-gamma)^{-1} epsilon^{-1})) iterations.
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Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
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Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs
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KL for a KL: On-Policy Distillation with Control Variate Baseline
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Convex Optimization with Nested Evolving Feasible Sets
For convex losses in nested evolving feasible sets, a lazy algorithm balances O(T^{1-β}) regret with O(T^β) movement for any β; for strongly convex or sharp losses, Frugal achieves zero regret with O(log T) movement, shown optimal by matching lower bound.
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Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective
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Theoretical Limits of Language Model Alignment
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Learning Visual Feature-Based World Models via Residual Latent Action
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ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
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Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
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Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent
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Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-Visual Language Models
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Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization
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Advantage-Guided Diffusion for Model-Based Reinforcement Learning
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Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
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Enhancing MLLM Spatial Understanding via Active 3D Scene Exploration for Multi-Perspective Reasoning
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Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
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TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
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GRAFT: Graph-Tokenized LLMs for Tool Planning
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Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs
An inexact augmented Lagrangian method with projected Q-ascent yields global last-iterate convergence guarantees for constrained MDP policy optimization, extending from tabular to log-linear and non-linear policies.
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UNIPO: Unified Interactive Visual Explanation for RL Fine-Tuning Policy Optimization
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Decaf: Improving Neural Decompilation with Automatic Feedback and Search
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Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
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