Pareto-Dominant Clarification: Post-Training Coding LLMs via PPO-Lagrangian Budget Constraints
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 w...
| Authors | Abhinav Rajput, Acey Vogelstein |
|---|---|
| Primary category | cs.LG |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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