# Pareto Optimal Code Generation

arXiv: https://arxiv.org/abs/2506.10056 · HTML: https://mlsanity.com/paper/2506.10056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- First posted on arXiv 11 Jun 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Built on by (most cited first, 1 of 1)

- [Test-Time Scaling Makes Overtraining Compute-Optimal](https://mlsanity.com/paper/2604.01411/) (2026)

## Abstract (arXiv)

Generate-then-rank is the dominant test-time scaling (TTS) paradigm for code generation, but scaling accuracy by sampling and executing more candidates makes comprehensive verification a major computational bottleneck. This creates an inherent trade-off between accuracy and compute that, despite its importance to TTS, is often ignored. Specifically, faster but noisier signals, such as outcome reward models (ORMs), are dismissed as suboptimal. We frame verifier selection as a Pareto optimization problem and empirically map the accuracy-throughput frontier across signals, including the full test suite, heuristics for selective execution, and ORMs, across four Python benchmarks. We show that ORMs are most effective at optimizing the Pareto curve when pruning is used in the generate-then-rank pipeline--known as staged verification--where lightweight filters remove obviously incorrect solutions, including candidates with small syntactic or character-level bugs, before expensive verification. Our pruning analysis shows that eliminating incorrect yet highly ranked candidates (often character-level bugs) prevents wasted compute on incorrect tokens. We find that ORMs with staged verification shift the Pareto frontier outward, achieving 11.64x higher throughput at a cost of 8.26% accuracy relative to full test-suite verification.