Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to encourage exploration but excessive entropy can cause instability, inefficient tool use and even training collapse. AEPO directly addresses these issues by introducing a balanced mechanism that intelligently manages entropy throughout the learning process.

At its core, it combines a Dynamic Entropy-Balanced Rollout with an Entropy-Balanced Policy Optimization strategy. This dual-phase approach enables models to explore high-uncertainty reasoning paths without losing stability or wasting computational resources. By penalizing redundant exploration and preserving meaningful gradients from high-entropy tokens, it allows AI agents to learn more efficiently and generalize better across multi-turn web reasoning tasks. The result is a smarter, faster and more reliable framework for training next-generation AI systems capable of deep information seeking and autonomous decision-making.

Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

Read the full paper on arXiv
Explore the official GitHub repository

The Problem with Traditional Agentic RL

In Agentic Reinforcement Learning (Agentic RL), models interact with dynamic environments such as search engines, browsers or code executors. While entropy-driven exploration helps the model learn new strategies too much entropy leads to two critical problems:

High-Entropy Rollout Collapse – The agent excessively branches out along uncertain trajectories, depleting computational resources on repetitive or low-value paths.
High-Entropy Token Gradient Clipping – During the policy update phase, gradients from high-entropy tokens are over-clipped hindering the model’s ability to learn from exploratory behaviors.

These issues cause instability in long-horizon reasoning and reduce the effectiveness of web agents, particularly in multi-turn interactions like complex searches or tool-based reasoning.

Introducing AEPO: A Balanced Approach to Entropy in RL

Agentic Entropy-Balanced Policy Optimization (AEPO) introduces an elegant dual-phase solution to tackle these challenges:

1. Dynamic Entropy-Balanced Rollout

AEPO implements a pre-monitoring mechanism that adaptively distributes exploration resources between global and branch-level rollouts. It evaluates token-level entropy to determine how uncertain a step is and then penalizes consecutive high-entropy tool calls to prevent over-branching.

This design ensures:

More diverse trajectory sampling
Balanced computational resource allocation
Reduced risk of rollout collapse

2. Entropy-Balanced Policy Optimization

Instead of uniformly clipping gradients, AEPO introduces a stop-gradient mechanism that preserves gradients of high-entropy tokens during backpropagation. It also includes entropy-aware advantage estimation allowing the model to prioritize learning from uncertain but informative steps.

This leads to:

Smoother gradient flow
Greater stability in training
Improved exploration efficiency

How AEPO Outperforms Other RL Methods ?

The researchers benchmarked AEPO against seven mainstream RL algorithms, including GRPO, ARPO and GPPO across 14 reasoning and web-search datasets such as GAIA, Humanity’s Last Exam and WebWalkerQA.

Key Results

With only 1K RL samples, Qwen3-14B + AEPO achieved:
- 47.6% on GAIA (Pass@1)
- 11.2% on Humanity’s Last Exam
- 43.0% on WebWalkerQA
On broader reasoning tasks, AEPO outperformed traditional RL methods by up to 5% in accuracy while maintaining higher rollout diversity.

Notably, AEPO also reduced tool calls by nearly 50% proving that intelligent entropy control not only improves performance but also cuts computational costs.

Why Entropy Balancing Matters in Web Agent Training ?

Entropy represents uncertainty. In web agent reinforcement learning, it signals when a model is unsure about the next tool call or reasoning step. While earlier algorithms like ARPO used entropy to trigger exploration, they lacked a mechanism to control how much exploration is appropriate.

AEPO’s innovation lies in treating entropy not as a one-way signal but as a dynamic balancing factor.
By adapting entropy budgets based on contextual uncertainty, AEPO enables the agent to:

Explore deeper when uncertainty is valuable
Converge faster when the policy becomes confident
Maintain stability throughout multi-turn reasoning

In essence, AEPO transforms entropy from a risk into a resource fostering scalable self-improving agents.

Applications and Future Directions

AEPO opens the door to a new generation of general-purpose AI agents capable of robust, autonomous reasoning across domains.

Potential applications include:

AI research assistants that interact with web APIs and databases to synthesize knowledge
Autonomous data analysts capable of querying live information for decision-making
Multi-tool AI systems for code execution, scientific discovery and academic search

Future research could explore AEPO’s integration with multi-modal models (text + vision) and continual learning frameworks expanding its reach beyond web-based agents into robotics, software automation and intelligent tutoring systems.

AEPO vs. ARPO: The Evolution of Agentic RL

Feature	ARPO	AEPO
Rollout Strategy	Entropy-guided branching	Dynamic entropy-balanced rollout
Policy Update	Uniform gradient clipping	Stop-gradient entropy-balanced optimization
Exploration Efficiency	High but unstable	High and stable
Tool-Call Usage	Redundant	Efficient and adaptive
Overall Performance	Strong baseline	Consistently superior across benchmarks

AEPO represents a second-generation Agentic RL approach – one that values not only exploration but balance, efficiency and control.

Conclusion

The launch of Agentic Entropy-Balanced Policy Optimization (AEPO) marks a significant milestone in the evolution of autonomous reasoning systems. By intelligently balancing entropy during rollout and policy updates, AEPO enables AI agents to explore effectively, learn efficiently and perform consistently.

As web agents continue to define the future of intelligent automation, AEPO stands as a cornerstone algorithm bridging the gap between high-entropy creativity and disciplined learning.

In the era of intelligent web agents, balance is power and AEPO achieves it with scientific precision.

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References

Read the research paper on arXiv
Explore the AEPO implementation on GitHub