PolyMind: Exploring Width Scaling for Reflective Reasoning in Language Agents

Reflection-based language agents improve reasoning by using feedback to revise previous attempts across multiple trials. Most existing reflection frameworks scale this process by \emph{depth}, allocating additional test-time compute to longer serial reflection-retry chains. However, we observe that serial reflection often saturates after rapid early gains. Under controlled reflection budgets, increasing depth from $5$ to $10$ cycles brings only marginal improvement, yet isolated reflective workers still contain useful revision signals beyond the saturated chain. This raises a central question: can reflective reasoning scale by \emph{width} rather than only by depth? A direct answer is non-trivial, since naive parallel reflection often inspects similar aspects of the same failed attempt and produces long or noisy revision contexts after aggregation. We propose \ourmethod, a reflective width scaling framework that learns to allocate complementary reflective scopes, execute workers in isolated contexts, and compress parallel feedback into a structured PolyBrief. \ourmethod trains the planner, workers, and reducer with verifiable outcome feedback and a role-balanced objective that prevents wider rollouts from dominating optimization. Across code generation, mathematical reasoning, and multi-hop question answering, \ourmethod consistently improves performance under matched reflection budgets, outperforming the strongest depth-reflection baseline by an average of $6.1\%$. Further analyses show that \ourmethod closes about $80\%$ of the oracle-naive width gap, reduces redundant worker outputs, and improves revision success with fewer tokens. These results suggest that reflection should be understood not only as a longer retry process, but also as a depth-width compute allocation problem.