This paper introduces GROW, an RL framework for open-world VLM agents, addressing limitations of existing Supervised Fine-Tuning methods. It proposes a novel approach for Group Relative Policy Optimization (GRPO) by decomposing trajectories into state-action samples rather than full entities.
This post details the implementation of verifiable rewards-based reinforcement learning (RLVR) to enhance training performance by ensuring transparency and correctness in reward signals. It covers techniques like GRPO and few-shot examples, demonstrated with the GSM8K dataset for improving math problem-solving accuracy.
StaRPO is a novel reinforcement learning framework designed to improve the logical consistency and structural coherence of large language models in complex reasoning tasks. It explicitly incorporates stability metrics, such as Autocorrelation Function and Path Efficiency, to evaluate local step-to-step coherence and global goal-directedness of the reasoning process.
RICE-PO is a novel critic-free policy optimization framework addressing the credit-assignment challenge in interactive language agents. It converts retrieval interactions into localized learning signals, evaluating executable actions and propagating credit to latent reasoning steps.
This research introduces Adaptive Power-Mean Policy Optimization (APMPO) to improve Large Language Model (LLM) reasoning capabilities within Reinforcement Learning with Verifiable Rewards (RLVR). APMPO combines a generalized power-mean objective and feedback-adaptive clipping to enhance learning dynamics and performance, addressing limitations of static optimization schemes.
O Reinforcement Learning é crucial para escalar modelos de linguagem, mas algoritmos existentes sofrem de instabilidade e colapso do modelo. Para resolver isso e permitir o escalonamento bem-sucedido, propõe-se o algoritmo Group Sequence Policy Optimization (GSPO).