Contrastive Language Model 发布：8B 参数，推理速度达 Jev 的 9 倍

CLM-8B 在互联网规模数据上预训练，推理速度比用 RLCD 训练的 Jev 快 9 倍，在 DeepSWE 上取得 81.6%、Terminal-Bench 2.

1 上取得 87.6% 的成绩。

CLM 通过嵌入状态和候选动作并比较相似度来排序选择，而 Jev 接收情境加预定义问题后返回带概率的类型化决策。作者还将状态与动作的嵌入分离缓存，在动作集固定、状态持续演化的场景下显著降低推理延迟。

团队还给出了 CLM 的缩放律：测试对比损失随训练算力、模型规模和数据量按幂律下降。

Pay attention to this new wave of System One models if you are building custom harnesses. First Jev. Now, Contrastive Language Model (CLM). CLM is 9x faster than Jev. CLM seems to be a better verifier than Jev, particularly at long-horizon tasks. How do Jev and CLM differ? CLM is contrastive, and Jev is trained with Reinforcement Learning for Calibrated Decisions (RLCD). Jev receives a situation plus predefined questions, and returns typed decisions with probabilities. CLM embeds the situation and candidate actions, compares their similarity, then ranks or selects the best match. The point is that there are several ways to attack this problem, which is exciting. You can see my recent guide on combining System One and System Two models for building custom harnesses. academy.dair.ai/resources/jev-… Jacky Kwok @jackyk02 Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions. CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks. With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks. We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed. Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size. 📄 Blog: contrastive-lm.notion.site x 💻 Code github.com/Contrastive-LM… 8I 🗣️ Discor discord.gg/5dAQEDJBs o3J 🤗 Data & Mode huggingface.co/Contrastive-LM O3hu More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵

本条情报归属 AITOP「模型」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：CLM、Jev、智能体、开源模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「elvis」（@omarsar0），经 AITOP 多维评分管线结构化提炼。