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Preprint Jul 2026

GFlowRL: Scaling Distribution-Matching RL to Large Language Models

GFlowRL, a streamlined GFlowNet-style RL algorithm that removes the auxiliary partition network entirely while preserving the reward-distribution-matching objective, is proposed, and is the first GFlowNet-style RL algorithm to scale stably across both dense and sparse architectures.

Xiaodong Liu, Michael Xu, Jack W. Stokes et al. · 0 citations

Dissecting Reinforcement Learning: Mechanisms Behind Compositional Reasoning in LLMs

This thesis proposes a unified two-axis framework that organizes SFT and RL methods along a data axis (off-policy to on-policy) and a loss function axis (positive-only to positive-plus-negative to GRPO) and enables controlled ablations of individual components.

G. Kim, Chair Chenyan Xiong, Aditi Raghunathan · 0 citations
Preprint Jul 2026

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

This work presents a stable and efficient training pipeline, incorporating algorithmic and system optimizations such as clipped importance sampling, training-inference ratio correction, and mixed-precision control, and proposes a structured evaluation framework across three dimensions: comprehensibility, reproducibility, and efficiency.

Xinyu Tang, Gangqiang Cao, Yurou Liu et al. · 0 citations
Preprint Aug 2026

Parameter Exploration for RLVR via Variational Learning

Evidence that parameter-space exploration can improve reinforcement learning for LLMs is presented, and a family of methods called Perturbed Parameter Policy Optimization (3PO) is introduced which use different sampling strategies and different rollout grouping for reward estimation.

Vatsal Venkatkrishna, Nico Daheim, Iryna Gurevych · 0 citations

VEG: Verbal ϵ -greedy for Semantic Exploration in Multi-Turn RL Agents

This work proposes VEG (verbal ϵ -greedy), a novel framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space and achieves superior accuracy compared to standard RL baselines.

Yongchang Hao, Jie Hao, Yongsheng Mei et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Q-Learning With World Models

This work proposes QWM, a framework that leverages world models to perform test-time search over imagined trajectories on top of Q-learning to select high-value actions during both online rollouts and evaluation, and significantly outperforms strong prior state-of-the-art methods on both sample efficiency and performance.

Perry Dong, Yueru Jia, Chelsea Finn et al. · 0 citations