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Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and tool-use in interactive environments. However, existing agent RL methods remain fundamentally constrained by an on-policy reasoning bottleneck: when tasks exceed the mode...
This work proposes ArenaFlow, a hierarchical credit propagation framework for open-ended agent reinforcement learning that leverages tournament-based relative ranking to derive trajectory-level reward signals and propagates trajectory-level advantages to high-confidence pivotal steps according to tournament survival de...
ARISE-RL, a novel full-cycle self-evolution framework that couples a task/rubric Generator and a reasoning Solver through rubric-mediated co-evolution, is proposed and ECR-Bench, an expert-calibrated rubric benchmark suite covering single-tool deep research and multi-tool travel planning is presented.
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