Can Smaller Models Help Larger Agents? Measuring the Incremental Value of Peer Advice

Whether smaller models can provide more effective corrections than self-revision for the failed tasks of large language model agents remains a critical question, one that distinguishes a model's independent problem-solving capability from cross-model collaborative value. This paper conducts fair comparisons between pure self-revision and peer-assisted revision under unified model checkpoints and fixed revision budgets, evaluating all outcomes on hidden test sets across 300 competitive-programming tasks to quantify the gains and risks of peer advice. Advice from an 8B model raises a 35B MoE agent's success from 52.0% to 64.3%, and the gain survives assigning each packet to a different task; in the reverse direction, collaboration depends critically on the task–advice assignment, separating 63.7% under matched advice from 54.0% under mismatched advice. On shared tasks,the two matching effects differ by 15.2 percentage points, a difference reproduced across seeds. Yet a partner-free review that keeps the revision instruction but removes all advice leaves matched advice with no detectable advantage on a fixed 150-task subset, so part of the collaborative gain reflects a productive revision workflow rather than advice content; adding verified repair steps to the same diagnosis still improves the 35B agent by 6.4–14.0 points across three seeds.Accordingly, this paper establishes a paired evaluation framework that separates workflow gains, task-matching value, and content value,measures the increment of peer advice over partner-free review, and quantifies both its benefits and risks, offering new insights for lightweight collaborative optimization of large language model agents.