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DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

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The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5.

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  • abstract The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have de

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Large Language Diffusion Models

cs.CL · 2025-02-14 · unverdicted · novelty 8.0

LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.

Smooth Scaling Laws Hide Stepwise Token Learning

cs.CL · 2026-06-29 · unverdicted · novelty 7.0

Token loss trajectories follow localized sigmoids whose learning-time spectrum quantitatively reconstructs scaling-law derivatives on T, D, and M axes and enables faster training via distribution reshaping.

Causal Bias Detection in Generative Artificial Intelligence

cs.AI · 2026-05-12 · unverdicted · novelty 7.0 · 2 refs

Develops a causal framework unifying generative AI fairness with standard ML, with new decompositions, identification conditions, and estimators demonstrated on LLM race and gender bias.

CodeMind: Evaluating Large Language Models for Code Reasoning

cs.SE · 2024-02-15 · unverdicted · novelty 7.0

CodeMind evaluates ten LLMs on four benchmarks using three new code reasoning tasks, finding performance varies by model size and drops with complexity while showing no correlation with bug repair ability.

Collective cooperation without individual fidelity in LLM agents

physics.soc-ph · 2026-06-29 · unverdicted · novelty 6.0

LLM agents reproduce macro-level cooperation decline and stabilization in networked Prisoner's Dilemma but underestimate individual heterogeneity and produce different conditional cooperation rules than humans.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers

cs.LG · 2026-05-29 · unverdicted · novelty 6.0

Contribution Weights combine attention, value magnitude, and directional alignment to measure token influence more faithfully than attention alone, and show attention sinks actively suppress information via a convex sink-rate to output-norm relationship.

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