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arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.
The paper introduces Document-Mediated Reinforcement Learning (DMRL), a framework that treats skill document optimization in advertising recommendation as a sequence of structured editing actions. An upper-level agent edits the skill documents while a frozen lower-level task agent evaluates the edits via A/B testing. DMRL incorporates Dual-Relative Policy Optimization for robust advantage estimation and a Long-term Reward Predictor that models population heterogeneity to estimate long-term outcomes, achieving superior performance on a large-scale short‑video ads platform.
Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored.
Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independent episodes, while existing approaches to skill learning either focus on repeated attempts of one task or use pipelines with multiple stages that entangle extraction, retrieval, and execution.
arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.
Writer‑R1 is a writing training pipeline that uses Inductive Rubric Generation (IRG) to automatically construct prompt‑specific rubrics for open‑ended writing tasks. The pipeline expands an open writing corpus, applies the generated rubrics during supervised training and reinforcement learning, and provides the rubrics to writers before generation. A compact model, RubricGen‑4B, can supply rubrics for new requests, and Writer‑R1 improves WritingBench and HelloBench scores over 4B and 8B backbones.