X-Coder: Advancing Competitive Programming with Synthetic Tasks, Solutions, and Tests

arXiv:2601.06953 · cs.CL, cs.LG · Submitted 2026-01-11 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-01-11

Updated: 2026-08-31

Comments: Accepted by EMNLP 2026 Main

Code: https://github.com/JieWu02/X-Coder

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Verification — 36× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Reinforcement Learning — 13× in this paper · explained in 4 episodes
• GRPO — 6× in this paper · explained in 6 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Reward Hacking — 3× in this paper · explained in 8 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 2× in this paper
• Reward Function — 2× in this paper · explained in 2 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Pruning — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• KL Regularization — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
• AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
• OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
• OpenThoughts: Data Recipes for Reasoning Models
• Evaluating Large Language Models Trained on Code
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Skywork Open Reasoner 1 Technical Report
• Process Supervision-Guided Policy Optimization for Code Generation
• Qwen2.5-Coder Technical Report
• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
• rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset
• AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
• LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
• The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason
• TACO: Topics in Algorithmic COde generation dataset
• Competition-Level Code Generation with AlphaCode
• Qwen2.5 Technical Report
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining

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