RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
RLAnything employs closed-loop optimization to dynamically shape environment, policy, and reward models, boosting performance on tasks like OSWorld by 9.1%.
Key Findings
Methodology
RLAnything integrates step-wise rewards, outcome rewards, and consistency feedback within a closed-loop framework. It employs algorithms such as Bayesian environment tuning and multi-task reward training to dynamically adjust environment difficulty based on critic feedback. The policy is trained with combined signals, while the reward model is refined via self-consistency. The environment adapts automatically, guided by critic evaluations, to optimize learning signals across complex agentic scenarios. This synergy enhances the overall RL system, demonstrated by performance gains on benchmarks like OSWorld, AlfWorld, and LiveBench, with improvements of 9.1%, 18.7%, and 11.9%, respectively.
Key Results
- RLAnything achieves a 9.1% accuracy increase on OSWorld, and boosts performance on AlfWorld and LiveBench by 18.7% and 11.9%. The reward model's step-wise signals outperform human labels, and environment adaptation accelerates training and improves generalization. Each component contributes significantly to the overall system enhancement, validated through ablation studies.
- The reward model's consistency feedback improves step-level prediction accuracy, while environment adjustment aligns task difficulty with model capability, facilitating long-trajectory learning. Experiments confirm that active environment adaptation from experience leads to more robust and generalizable models.
- Across diverse applications—GUI control, text-based games, coding—the framework demonstrates broad applicability, consistently outperforming static RL baselines. The integrated approach enhances model robustness, scalability, and efficiency, paving the way for RL in real-world, multi-modal, multi-task settings.
Significance
This work addresses longstanding challenges in RL, notably reward sparsity and environment tuning, by proposing a unified, adaptive system. It bridges the gap between static training paradigms and real-world needs for autonomous, scalable learning. The theoretical guarantees and empirical results demonstrate that dynamic environment shaping and reward refinement significantly improve learning efficiency and model generalization, especially in complex, long-horizon tasks. This advances RL research towards more autonomous, intelligent systems capable of continuous self-improvement in diverse scenarios, with broad implications for AI deployment in industry and research.
Technical Contribution
The core technical contributions include: 1) the RLAnything framework integrating environment, policy, and reward models in a closed-loop system; 2) novel automatic environment adaptation driven by critic feedback, enabling task difficulty regulation; 3) multi-source reward training combining outcome and step-wise signals, with theoretical proofs of reward signal quality and convergence guarantees. The system leverages large multimodal models like Qwen, demonstrating scalable, high-performance training across multiple tasks. These innovations collectively push the boundaries of RL, offering a new paradigm for scalable, autonomous learning.
Novelty
This is the first comprehensive system that dynamically shapes environment, policy, and reward models simultaneously in a closed-loop manner. Unlike prior work that treats these components separately or statically, RLAnything introduces an automatic environment adjustment mechanism based on critic feedback, ensuring balanced task difficulty and improved reward signals. Its integration of multi-source reward signals with theoretical validation distinguishes it from existing RL approaches, representing a significant step forward in adaptive, scalable reinforcement learning.
Limitations
- The system heavily depends on critic feedback accuracy; in highly unpredictable or noisy environments, feedback errors could destabilize environment adaptation.
- Computational costs are high due to large model sizes and frequent environment adjustments, limiting real-time deployment in resource-constrained settings.
- Current validation is primarily in simulated environments; real-world robustness and scalability require further testing and optimization.
Future Work
Future research will focus on reducing computational overhead, enhancing stability of environment adaptation, and extending the framework to real-world robotics and multi-modal data. Incorporating meta-learning techniques could further improve autonomous environment tuning. Additionally, exploring more efficient reward signal estimation and transfer learning strategies will broaden applicability, aiming for deployment in dynamic, real-world scenarios such as autonomous vehicles and industrial automation.
AI Executive Summary
RLAnything introduces a groundbreaking reinforcement learning framework that dynamically shapes environment, policy, and reward models through a closed-loop optimization process. Traditional RL approaches often rely on static environments and sparse rewards, which hinder efficient learning in complex, long-horizon tasks. By integrating step-wise rewards, outcome signals, and consistency feedback, RLAnything creates an adaptive system capable of automatic environment adjustment based on critic feedback. This mechanism ensures task difficulty remains aligned with the model’s evolving capabilities, significantly improving training efficiency and generalization.
The core innovation lies in the seamless coupling of environment, policy, and reward models, each continuously informing and refining the others. The policy benefits from rich, multi-source signals, while the reward model is refined via self-consistency, leading to more reliable step-wise evaluation. The environment adapts automatically, guided by critic feedback, to maintain optimal learning conditions. Empirical results across diverse tasks—such as OSWorld, AlfWorld, and coding benchmarks—demonstrate substantial performance gains, with accuracy improvements of up to 18.7%. Notably, the reward signals generated by the system outperform human labels, highlighting its potential for active, experience-driven learning.
This framework addresses key bottlenecks in RL, notably reward sparsity and environment tuning, paving the way for more autonomous, scalable AI systems. Its theoretical foundations guarantee convergence and reward quality, while practical experiments validate its broad applicability. The ability to learn from experience and actively adjust tasks makes RLAnything a promising step toward intelligent systems capable of continuous self-improvement in real-world scenarios. Future directions include reducing computational costs, extending to physical robots, and integrating multi-modal data, aiming for robust deployment in industry and research. Overall, RLAnything marks a significant advance in adaptive reinforcement learning, with profound implications for AI development.
Deep Dive
Abstract
We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signals and strengthening the overall RL system for any LLM or agentic scenarios. Specifically, the policy is trained with integrated feedback from step-wise and outcome signals, while the reward model is jointly optimized via consistency feedback, which in turn further improves policy training. Moreover, our theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience. Empirically, each added component consistently improves the overall system, and RLAnything yields substantial gains across various representative LLM and agentic tasks, boosting Qwen3-VL-8B-Thinking by 9.1% on OSWorld and Qwen2.5-7B-Instruct by 18.7% and 11.9% on AlfWorld and LiveBench, respectively. We also that optimized reward-model signals outperform outcomes that rely on human labels. Code: https://github.com/Gen-Verse/Open-AgentRL