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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning7h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks7h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts7h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning7h◆FrontierChallenge: Evaluating Scientific Workflow Completion7h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier7h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising7h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic7h◆Omni Interaction Agent Technical Report7h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification7h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability7h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization7h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding7h◆Tracing Computation Density in LLMs7h◆Cultural Binding Heads in Language Models7h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training7h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models7h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning7h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection7h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation7h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning7h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks7h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts7h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning7h◆FrontierChallenge: Evaluating Scientific Workflow Completion7h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier7h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising7h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic7h◆Omni Interaction Agent Technical Report7h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification7h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability7h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization7h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding7h◆Tracing Computation Density in LLMs7h◆Cultural Binding Heads in Language Models7h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training7h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models7h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning7h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection7h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation7h◆
News/model/Qwen3.5-2B-Base

Qwen3.5-2B-Base news

3 articles mentioning Qwen3.5-2B-Base

arxivJul 3

TUDUM: A Turkish-Thinking Reasoning Pipeline for Qwen3.5-27B

arXiv:2607.01927v1 Announce Type: cross Abstract: This paper presents TUDUM (T\"urk\c{c}e D\"u\c{s}\"unen \"Uretken Model), a project pipeline for adapting a Qwen-family 27B thinking model toward Turkish reasoning. The central problem is not only to answer Turkish prompts in Turkish, but to make the

arxivMay 15

Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models

arXiv:2605.11907v2 Announce Type: replace Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as a frontier reference. The corpus is 353 rows of (task + procedural-skill block, Opus chain-of-t

arxivApr 22

Qwen3.5-Omni Technical Report

arXiv:2604.15804v2 Announce Type: replace Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor, Qwen3.5-Omni scales to hundreds of billions of parameters and supports a 256k context length. By

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