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Post-Training Language Models for Gold-Medal Performance in Coding Competitions

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its most challenging settings. We present an end-to-end specialization pipeline combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning (SFT), and reinforcement learning (RL). Using 22,000 curated problems, we train Ne

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#203 most recent of 300 cs.AI papers we have recorded · ↑ newer: Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decisio · ↓ older: AI Contextual Measurement for Recovering Individual and Group-Level Ef
Cite this page: Post-Training Language Models for Gold-Medal Performance in Coding Competitions: the #203 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/post-training-language-models-for-gold-medal-performance-in-coding-competitions.html
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