OpenPipe ART: Agent Reinforcement Trainer for Multi-Step Agents via GRPO
OpenPipe has released ART (Agent Reinforcement Trainer), an open-source Python library for training multi-step agents on real-world tasks using GRPO (Group Relative Policy Optimization). The framework supports multiple model families including Qwen3, GPT-OSS, and Llama. With nearly 10k GitHub stars and 66 gained today, it is gaining notable community traction as a practical RL fine-tuning tool for agentic workflows.
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Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective
A Hugging Face blog post authored by LinkedIn describes practical lessons from implementing reinforcement learning training for agentic open-source GPT-class models. The retrospective covers engineering and algorithmic challenges encountered when applying RL to agentic workflows. As a tier-2 source with no body content available, the depth and specific findings cannot be fully assessed, but the topic sits at the intersection of agentic systems and RLHF/RL training pipelines.
GSPO: Group Sequence Policy Optimization for Scalable RL Training of Language Models
Qwen researchers introduce Group Sequence Policy Optimization (GSPO), a new RL algorithm designed to address severe training instability and model collapse observed in existing methods like GRPO during extended training runs. The core motivation is enabling stable RL scaling for language models to improve reasoning and problem-solving capabilities with increased compute. The paper targets a known bottleneck in post-training pipelines where instability prevents further performance gains.
OpenAI Releases Proximal Policy Optimization (PPO)
OpenAI introduced Proximal Policy Optimization (PPO), a new class of reinforcement learning algorithms that match or exceed state-of-the-art performance while being simpler to implement and tune. PPO was adopted as OpenAI's default RL algorithm due to its balance of ease of use and strong performance. The release marked a significant methodological contribution to the RL field that would go on to underpin many subsequent AI training pipelines.
AgenticRL: Self-refining LLM-guided reward design and policy refinement for UAV navigation
AgenticRL is a framework that uses a multimodal GPT agent to automate reward function generation, policy training via PPO, and closed-loop self-refinement for UAV navigation tasks. The agent evaluates trained policies through diagnostic feedback, identifies failure modes, and iteratively refines rewards without human intervention. Evaluated across five navigation tasks, the closed-loop refinement improves policy behavior by 71% over initial rewards, with sim-to-real transfer achieving 91% real-world success rate and 94% sim-to-real accuracy.
General Preference Reinforcement Learning (GPRL): Bridging Online RL and Preference Optimization for Open-Ended Tasks
GPRL proposes a new alignment framework that replaces scalar reward models with a General Preference Model (GPM) embedding responses into k skew-symmetric subspaces to capture multi-dimensional, intransitivity-aware preferences. The method computes per-dimension group-relative advantages, normalizes across axes, and uses a closed-loop drift monitor to detect and correct single-axis reward hacking during training. Starting from Llama-3-8B-Instruct, GPRL achieves a 56.51% length-controlled win rate on AlpacaEval 2.0 and outperforms SimPO and SPPO on Arena-Hard, MT-Bench, and WildBench. The work directly addresses the gap between verifiable-reward online RL (strong on math/code) and preference optimization (strong on open-ended tasks).
AXPO: Agent Explorative Policy Optimization Addresses Thinking-Acting Gap in Multimodal Agentic Reasoning
This paper identifies a structural asymmetry in agentic reasoning called the 'Thinking-Acting Gap,' where tool use is attempted in only ~30% of rollouts under standard RL training (GRPO), and all-wrong tool-using subgroups suppress learning signals. The authors propose AXPO (Agent eXplorative Policy Optimization), which fixes the thinking prefix and resamples tool calls for all-wrong subgroups, combined with uncertainty-based prefix selection. Evaluated across nine multimodal benchmarks on Qwen3-VL-Thinking at multiple scales, SFT+AXPO outperforms SFT+GRPO by +1.8pp on both Pass@1 and Pass@4 at 8B, with the 8B model surpassing the 32B baseline on Pass@4 using 4× fewer parameters.
APPO: Fine-grained branching and credit assignment for agentic RL in LLMs
Researchers introduce Agentic Procedural Policy Optimization (APPO), a reinforcement learning method that shifts branching and credit assignment from coarse tool-call boundaries to fine-grained decision points within generated sequences. APPO uses a Branching Score combining token uncertainty with policy-induced likelihood gains to select exploration points, plus procedure-level advantage scaling for credit distribution. Evaluated on 13 benchmarks, APPO improves strong agentic RL baselines by nearly 4 points while maintaining efficient tool use and interpretability. The work addresses a known weakness in multi-turn agentic RL: that influential decisions are distributed throughout sequences, not concentrated at tool-call boundaries.
AdvGRPO: Stable co-training framework for adaptive red teaming of language models
Researchers introduce AdvGRPO, a co-training framework that makes GRPO viable for joint attacker-defender optimization in LLM red teaming, addressing previously reported instability. The method uses dense multi-channel rewards and decoupled advantage normalization, with a curriculum progressing from single-turn to multi-turn attacks before bootstrapping co-training. Co-trained defenders outperform baselines on safety benchmarks, and the attacks show transferability across models.