Pareto-Dominant Clarification: Post-Training Coding LLMs via PPO-Lagrangian Budget Constraints

Abhinav Rajput, Acey Vogelstein

Coding agents operating under ambiguous instructions or user prompts must decide whether to ask clarifying questions or attempt a solution directly. While clarification from the user may improve the correctness of the agent's solution, each back-and-forth interaction incurs user and system costs, forming an explicit accuracy vs. efficiency tradeoff. Existing works study clarification behavior but do not train policies under enforceable clarification budgets; penalty-based approaches typically require separate coefficient tuning swept across all clarification budget levels. We formulate clarification as a Constrained Markov Decision Process (CMDP) and post-train Qwen2.5-Coder-7B-Instruct with PPO-Lagrangian to optimize coding accuracy, subject to an expected question-budget constraint. Evaluated on HumanEvalComm with a GPT-4o-mini oracle simulator, the resulting policies reveal that untuned clarification behavior is Pareto-inefficient: budget-constrained policies can simultaneously achieve higher accuracy and lower clarification rates than the baseline model. Across budget levels, we observe a log-shaped Pareto frontier with diminishing returns to additional clarification. Gains…