aiqbench analysis
This paper addresses a critical bottleneck in training long-horizon LLM agents by proposing Guide Assisted GRPO, which mitigates the lack of reward signals during early training phases where all rollouts fail. The study demonstrates that strategic curriculum choices, such as starting agents near task completion and using outcome-informed tool selection, significantly improve performance on benchmarks like BFCL and AppWorld compared to standard GRPO baselines. These findings offer practical guidance for optimizing reinforcement learning trajectories in complex agent environments.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents
arXiv:2610.19349v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) provides no reward-relative learning signal when all rollouts receive identical rewards. In long-horizon tasks that require multiple interactions, rewards are given only upon successful task completion, making all-fail groups common early in training and limiting opportunities to practice later steps. Guide Assisted GRPO replays a successful reference prefix before the learner continues. We study how initial positions, handoff units, and movement rules affect unassisted task completion. Across three model sizes on BFCL, starting near completion is more effective under a fixed turn-level threshold rule. Finer handoffs alone do not consistently help, but outcome-informed tool-call selection outperforms GRPO on trained and untrained task categories. Guided starts also improve AppWorld success on both evaluation splits. These findings highlight where learning starts and how starting positions move as key curriculum choices.
Abstract:Group Relative Policy Optimization (GRPO) provides no reward-relative learning signal when all rollouts receive identical rewards. In long-horizon tasks that require multiple interactions, rewards are given only upon successful task completion, making all-fail groups common early in training and limiting opportunities to practice later steps. Guide Assisted GRPO replays a successful reference prefix before the learner continues. We study how initial positions, handoff units, and movement rules affect unassisted task completion. Across three model sizes on BFCL, starting near completion is more effective under a fixed turn-level threshold rule. Finer handoffs alone do not consistently help, but outcome-informed tool-call selection outperforms GRPO on trained and untrained task categories. Guided starts also improve AppWorld success on both evaluation splits. These findings highlight where learning starts and how starting positions move as key curriculum choices.
7 pages, 4 figures, 4 tables. Minchan Kim and Sungho Bae contributed equally
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2610.19349 [cs.LG]
(or arXiv:2610.19349v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19349 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 244 words
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