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This research investigates paired rollouts as a method to mitigate noise in group-relative reinforcement learning for LLM agents, specifically addressing how shared event-keyed schedules affect reward-contrast variance. While the study demonstrates reduced estimator variance under tool faults and grader flips, it notes that these improvements do not always translate to faster learning curves or significant gains in validation AUC. The work provides critical theoretical conditions and empirical evidence on when noise-reduction techniques effectively stabilize training without guaranteeing generalizable speed benefits.
Luck Is Not Skill: When Do Paired Rollouts Help Group-Relative RL of LLM Agents?
arXiv:2609.24144v1 Announce Type: new Abstract: Group-relative reinforcement learning compares rollouts of the same prompt, but independent environment noise can obscure these comparisons. We study paired rollouts, which share an event-keyed noise schedule within each group while preserving each rollout's marginal distribution. Pairing removes the between-schedule component of reward-contrast variance, but need not reduce gradient variance. For one-sided grader noise, we derive an exact condition for reduction and give a counterexample in which reward contrasts improve while gradient variance increases. A controlled study trains a 2B tool-use agent under tool faults and grader flips, with three seeds per design. The protocol was registered with a disclosed, previously completed pilot. Under tool faults, pairing improves final noisy-test success by +5.1 percentage points on average, with all three seed differences positive, but misses the registered learning-curve criterion. The criterion is also missed under grader flips: the validation-AUC difference is +0.003 (95% interval [-0.029, +0.033]). A gradient probe on eight distinct checkpoints from two fault-trained trajectories finds lower mean-centered covariance traces under both noise types: 21 to 30% for grader flips and 40 to 63% for tool faults. These finite-sample measurements support the variance mechanism without establishing a general learning-speed benefit. The results distinguish improving reward comparisons, reducing estimator variance, and improving learning.
Title:Luck Is Not Skill: When Do Paired Rollouts Help Group-Relative RL of LLM Agents?
Abstract:Group-relative reinforcement learning compares rollouts of the same prompt, but independent environment noise can obscure these comparisons. We study paired rollouts, which share an event-keyed noise schedule within each group while preserving each rollout's marginal distribution. Pairing removes the between-schedule component of reward-contrast variance, but need not reduce gradient variance. For one-sided grader noise, we derive an exact condition for reduction and give a counterexample in which reward contrasts improve while gradient variance increases. A controlled study trains a 2B tool-use agent under tool faults and grader flips, with three seeds per design. The protocol was registered with a disclosed, previously completed pilot. Under tool faults, pairing improves final noisy-test success by +5.1 percentage points on average, with all three seed differences positive, but misses the registered learning-curve criterion. The criterion is also missed under grader flips: the validation-AUC difference is +0.003 (95% interval [-0.029, +0.033]). A gradient probe on eight distinct checkpoints from two fault-trained trajectories finds lower mean-centered covariance traces under both noise types: 21 to 30% for grader flips and 40 to 63% for tool faults. These finite-sample measurements support the variance mechanism without establishing a general learning-speed benefit. The results distinguish improving reward comparisons, reducing estimator variance, and improving learning.
24 pages, 5 figures, 14 tables. Code, task pools, pre-registration and numerical summaries: this https URL
Machine Learning (cs.LG)
arXiv:2609.24144 [cs.LG]
(or arXiv:2609.24144v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.24144 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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