# Scaling Agentic Verifier for Competitive Coding

arXiv: https://arxiv.org/abs/2602.04254 · HTML: https://mlsanity.com/paper/2602.04254/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 32 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 30)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy](https://mlsanity.com/paper/2507.01352/) (2025)
- [Natural Language to Code Translation with Execution](https://mlsanity.com/paper/2204.11454/) (2022)
- [Coder Reviewer Reranking for Code Generation](https://mlsanity.com/paper/2211.16490/) (2022)
- [Can Language Models Solve Olympiad Programming?](https://mlsanity.com/paper/2404.10952/) (2024)
- [LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?](https://mlsanity.com/paper/2506.11928/) (2025)
- [The Limits of Inference Scaling Through Resampling](https://mlsanity.com/paper/2411.17501/) (2024)
- [Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions](https://mlsanity.com/paper/2312.12450/) (2023)
- [Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency](https://mlsanity.com/paper/2309.17272/) (2023)
- [Language Models for Code Completion: A Practical Evaluation](https://mlsanity.com/paper/2402.16197/) (2024)
- [Fault-Aware Neural Code Rankers](https://mlsanity.com/paper/2206.03865/) (2022)
- [Dynamic Scaling of Unit Tests for Code Reward Modeling](https://mlsanity.com/paper/2501.01054/) (2025)
- [SWE-RM: Execution-free Feedback For Software Engineering Agents](https://mlsanity.com/paper/2512.21919/) (2025)
- [OJBench: A Competition Level Code Benchmark For Large Language Models](https://mlsanity.com/paper/2506.16395/) (2025)
- [PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization](https://mlsanity.com/paper/2511.00010/) (2025)
- [ICPC-Eval: Probing the Frontiers of LLM Reasoning with Competitive Programming Contests](https://mlsanity.com/paper/2506.04894/) (2025)
- [From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning](https://mlsanity.com/paper/2601.13384/) (2026)
- [Disproving Program Equivalence with LLMs](https://mlsanity.com/paper/2502.18473/) (2025)

## Built on by (most cited first, 7 of 7)

- [Dockerless: Environment-Free Program Verifier for Coding Agents](https://mlsanity.com/paper/2606.28436/) (2026)
- [GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning](https://mlsanity.com/paper/2604.02721/) (2026)
- [RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning](https://mlsanity.com/paper/2604.00790/) (2026)
- [Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling](https://mlsanity.com/paper/2605.11299/) (2026)
- [Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification](https://mlsanity.com/paper/2609.38812/) (2026)
- [Verification Trap: Understanding Test-Time Selection Failures under False Premises in Code Generation](https://mlsanity.com/paper/2610.05170/) (2026)
- [Beyond the Verdict: Agentic Code Judging with Verifiable Feedback](https://mlsanity.com/paper/2610.18760/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling. To address this limitation, we propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs that expose behavioral discrepancies among candidate solutions. Through multi-turn interaction with code execution environments, the verifier iteratively refines the candidate input generator and produces targeted counterexamples rather than blindly sampling inputs. We train the verifier to acquire this discriminative input generation capability via a scalable pipeline combining large-scale data synthesis, rejection fine-tuning, and agentic reinforcement learning. Extensive experiments across five competitive programming benchmarks demonstrate consistent improvements over strong execution-based baselines, achieving up to +10-15% absolute gains in Best@K accuracy. Further analysis reveals clear test-time scaling behavior and highlights the verifier's broader potential beyond reranking.