英伟达4B小模型登顶ARC-AGI 2！零预训练策略成本优势显著

ITBEAR科技资讯2025-12-08

在人工智能模型性能比拼的赛场上，英伟达推出的4B小模型NVARC在ARC-AGI 2测试中脱颖而出，以27.64%的公开榜成绩超越GPT-5 Pro的18.3%，成功登顶榜首。这一成绩不仅展现了小模型的强大潜力，也引发了业界对模型发展方向的新思考。NVARC的显著优势不仅体现在成绩上，更在于其成本控制。该模型每任务成本仅20美分，而GPT-5 Pro单任务成本超过7美元，NVARC的成本仅为前者的...

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The zero pre-training strategy has significant cost advantages","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDS.UK":0.6,"NVD2.UK":0.6,"SNVD.UK":0.6,"3NVD.UK":0.6,"NVDU":0.6,"NVDL":0.6,"NVDX":0.6,"NVDY":0.6,"NVDD":0.6,"NVDA":1.97,"NVD":0.6,"2NVD.UK":0.6,"NVD3.UK":0.6,"NVIW.SI":0.6,"NVDS":0.6},"content_text":"在人工智能模型性能比拼的赛场上，英伟达推出的4B小模型NVARC在ARC-AGI 2测试中脱颖而出，以27.64%的公开榜成绩超越GPT-5 Pro的18.3%，成功登顶榜首。这一成绩不仅展现了小模型的强大潜力，也引发了业界对模型发展方向的新思考。NVARC的显著优势不仅体现在成绩上，更在于其成本控制。该模型每任务成本仅20美分，而GPT-5 Pro单任务成本超过7美元，NVARC的成本仅为前者的1/36。这种低成本特性使其在实际应用中更具竞争力，尤其是在资源有限的场景下。NVARC的成功得益于其独特的零预训练深度学习方法。与依赖大规模通用数据集进行前期预训练的模型不同，NVARC规避了预训练模型可能存在的领域偏见和数据依赖问题。这种方法使其在ARC-AGI 2测试中表现出色，该测试旨在评估模型能否高效获取超出其训练数据的新技能，且测试数据与公共训练数据无重叠。为了实现这一突破，英伟达团队采取了创新的策略。他们将复杂推理转移至离线的合成数据管道，通过大规模合成高质量数据来优化现有模型。具体而言，团队利用GPT-OSS-120B生成了320万多个增强样本的合成数据集，每个样本最多包含7对输入/输出。这一过程通过拆分推理管线为多个独立验证的阶段，确保了数据质量。在模型选择上，NVARC以改进版ARChitects方法为基础，选用小参数模型Qwen3-4B，并通过对话式模板简化谜题理解。训练过程中，团队借助NeMo RL框架和Megatron后端进行监督微调。而真正让模型取得优异成绩的关键在于测试时微调（TTFT）技术。针对ARC-AGI-2“每个任务都是全新规则”的特点，NVARC引入了LoRA微调技术，对每个问题单独微调，使模型能快速适应新任务。团队对ARChitects方法的解码阶段进行了优化，通过批处理DFS算法修复结果非确定性问题，并统一了8种数据增强操作评估候选解。这些改进共同助力NVARC在公开榜上获得27.64%的分数。在竞赛后期，团队还尝试了“少即是多”的TRM方法，与Qwen3-4B集成以进一步提升分数。尽管这一方法带来了一定提升，但受限于多种因素，并未实现大幅优化。有人质疑，这样训练出来的小模型是否只是“做题机器”，难以与全面发展的超级大模型相提并论。然而，NVARC的成功表明，在特定领域任务中，经过针对性优化的小模型性能并不逊色。其低成本、高速度、强适配性和领域聚焦等优势，使其在诸多场景中展现出巨大潜力。这一成果提醒我们，在模型开发中，将正确的方法应用于正确的场景，往往能实现更大的价值。小模型的崛起，或许将为人工智能的发展开辟新的路径。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":1,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)","isDev":false,"isTTM":false,"tenantId":"TBCN","deviceId":"web-server-community-laohu8-v3","version":"4.36.2","shortVersion":"4.36.2","platform":"web","vendor":"web","appName":"laohu8","isIOS":false,"isAndroid":false,"isTiger":false,"isTHS":false,"isWeiXin":false,"isWeiXinMini":false,"isWeiBo":false,"isQQ":false,"isBaiduSwan":false,"isBaiduBox":false,"isDingTalk":false,"isToutiao":false,"isOnePlus":false,"isHuaWei":false,"isXiaomi":false,"isXiaomiWebView":false,"isOppo":false,"isVivo":false,"isSamsung":false,"isMobile":false},"href":"/m/news/2589387471","isCrawlerRequest":true}