… The following papers were recommended by the Semantic Scholar API Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes 2026 Hybrid Policy Distillation for LLMs 2026 Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation 2026 A Su… …
Papers arxiv:2604.26091 Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital Published on Apr 28 Submitted by Poof on Apr 30 DXRG AI Inc Authors: , , , , , , Abstract Autonomous language-model agents managing real cryptocurrency trades demonstrated high reliability through … …
… To address this issue, we propose Token- and Turn-level Policy Optimization T^2PO , an uncertainty-aware framework that explicitly controls exploration at fine-grained levels. …
… The following papers were recommended by the Semantic Scholar API PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners 2026 Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR 2026 On-Policy Distillation with Best-of-N Teac… …
… Three modules in LiSA: ① Broad policy abstraction — turn sparse failures into reusable policies ② Conflict-aware local policies — preserve boundary cues in mixed-label regions where a single broad rule would overgeneralize ③ Evidence-aware confidence gating — Beta posterior lower bound, so "validat… …
… The following papers were recommended by the Semantic Scholar API Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration 2026 PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners 2026 GRPO-VPS: Enhancing Group Relative Policy Op… …
… The following papers were recommended by the Semantic Scholar API Flow Matching Policy with Entropy Regularization 2026 ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching 2026 Truncated Rectified Flow Policy for Reinforcement Learning with One-Step … …
… AI-generated summary Reinforcement learning RL has enabled complex reasoning abilities in large language models LLMs . However, most RL algorithms suffer from performance saturation , preventing continued gains as RL training scales. …
… The evaluation shows three main results: learned routing reaches a higher-quality operating point than a fixed pipeline baseline on coordination-heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm-started policy graphs can reduce exploration cost whil… …
… The following papers were recommended by the Semantic Scholar API Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models 2026 ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation 2026 $M^2$-VLA: Boosting Vision-Language Models for… …