Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks
Researchers propose Progress-conditioned Group Policy Optimization (ProGPO) to enhance the training of large language model (LLM) agents on complex, long-horizon tasks. This method addresses sampling imbalance by using first-visit observation coverage when traditional out…
Intelligence analysis by Gemini 2.5 Flash
Group-based policy optimization for LLM agents often struggles with long-horizon tasks due to a 'credit trap' where low-effect actions dominate sampling, leading to all-failed groups with no useful feedback. ProGPO tackles this by assigning higher relative advantages to trajectories that explore new states, specifically when traditional outcome rewards offer no guidance, thereby break…
Imagine you're trying to find a hidden treasure in a big house, but you only get a "yay!" if you find it, and nothing if you don't. If you keep looking in the same empty rooms, you'll never find it. This new AI trick, called ProGPO, helps the AI agent. If it tries a bunch of things and gets no "yay!" at all, it then looks at which tries explored new rooms. It learns that even if it didn't find the treasure, exploring new places is a good step, so it tries to explore more next time. This helps it stop getting stuck and eventually find the treasure!
Analysis
The core problem ProGPO addresses is the "credit trap" in group-based policy optimization for LLM agents. When agents tackle long-horizon tasks, many actions might not immediately lead to a reward, or they might be repetitive and low-impact. This creates a sampling imbalance where the policy frequently samples these ineffective actions, leading to groups of rollouts where all attempts fail and yield zero outcome reward. Without any positive or negative signal, the policy has no direction to correct itself, perpetuating the cycle of failure.
Breaking the Credit Trap
ProGPO introduces a novel mechanism to provide useful feedback even when all trajectories in a group receive zero outcome reward. Instead of relying solely on sparse outcome rewards, it leverages "first-visit observation coverage." This means that within groups where all attempts fail, ProGPO assigns higher relative advantages to trajectories or steps that visit more new states. The rationale is that exploring new states is a fundamental prerequisite for making progress and eventually achieving task success, even if that success isn't immediately apparent. By rewarding exploration in these "all-failed" scenarios, ProGPO encourages the agent to break out of repetitive, unproductive action sequences.
Enhancing Agent Exploration
The method specifically intervenes when the traditional reward signal is absent, preventing the policy from getting stuck in local optima dominated by low-effect actions. This targeted intervention helps agents explore the environment more effectively, discovering paths and actions that might eventually lead to success. The paper's experiments on challenging benchmarks like ALFWorld and WebShop, using Qwen2.5-1.5/7B-Instruct, demonstrate that ProGPO consistently outperforms existing group-based baselines. The gains are particularly pronounced on harder tasks, indicating its effectiveness in scenarios where agents are most likely to encounter the credit trap.
Implications for Long-Horizon Tasks
The ability to effectively train agents for long-horizon tasks is crucial for developing truly autonomous AI systems. Many real-world problems, from complex coding tasks to multi-step robotic manipulation, require a sequence of actions where immediate rewards are rare. ProGPO's approach of conditioning policy optimization on progress, even in the absence of immediate success, offers a promising direction. It highlights the importance of intrinsic motivation and exploration in reinforcement learning, especially when dealing with sparse reward environments. This research could pave the way for LLM agents that are more adept at planning, problem-solving, and adapting to novel situations over extended periods, moving closer to human-like intelligence in complex domains.
Key points
- ProGPO is a new method for training large language model (LLM) agents on long-horizon tasks.
- It addresses the "credit trap" where sparse outcome rewards lead to policies stuck on low-effect actions.
- ProGPO uses first-visit observation coverage to provide feedback when all group samples yield zero outcome reward.
- It assigns higher relative advantages to trajectories that visit more new states in such failure scenarios.
- Experiments on ALFWorld and WebShop benchmarks show consistent improvements over baselines, especially on hard tasks.
ProGPO's ability to improve LLM agent performance on challenging long-horizon tasks suggests a future where AI agents can tackle more complex, multi-step problems with greater autonomy and efficiency. This could lead to breakthroughs in areas requiring sophisticated planning and exploration, such as advanced robotics, scientific discovery, and intelligent assistants capable of handling intricate user requests.
While promising, the reliance on "first-visit observation coverage" might still face challenges in extremely vast or highly redundant environments where defining "new states" is ambiguous or where exploration itself becomes computationally prohibitive. If the definition of progress is not robust, agents might still get stuck in unproductive exploration loops, albeit different ones, or struggle with tasks requiring very specific, non-obvious sequences of actions.
