… The following papers were recommended by the Semantic Scholar API PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training 2026 Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model 2026 Accelerating RL Post-Training Rollouts via System-Integ… …
… AI-generated summary RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation , making rollout acceleration a central systems challenge. …
… One approach, reinforcement learning, rewards the model through trial and error and can work well, but it is costly because the model must repeatedly generate full back-and-forth conversations during training. …
… Stream-R1 instead reweights the DMD objective along two complementary axes, i.e., Inter-Reliability across rollouts and Intra-Perplexity across spatiotemporal regions, both with a single shared video reward model. …
… World models can generate concrete visual rollouts of possible futures, while MLLMs can reason abstractly over questions, goals, and rules. …
… We propose three exact old-logit acquisition strategies: snapshot-based version tracking, a dedicated old-logit model, and synchronization via partial rollout interruption, and compare their system trade-offs. …
… AI-generated summary Reinforcement learning with verifiable rewards RLVR for Large Reasoning Models hinges on baseline estimation for variance reduction , but existing approaches pay a heavy price: PPO requires a policy-model scale critic , while GRPO needs multiple rollouts per prompt to keep its … …
Papers arxiv:2606.02388 Policy and World Modeling Co-Training for Language Agents Published on Jun 1 Submitted by Ning Lu on Jun 2 HKUST Authors: Ning Lu , , , , , , , , , , , Abstract PaW is a co-training framework that combines policy learning and world modeling using on-policy reinforcement lear… …
… Then, we train a video diffusion model to generate these tokens using coarse-to-fine rollout . …
… The following papers were recommended by the Semantic Scholar API Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation 2026 Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training 2026 ADWIN: Adaptive Windows … …
