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Qwen3.5-2B-Base
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qwen3-5-2b-base-bf51dac9·5 events·first seen Aliases: Qwen3.5-2B-Base, Qwen3.5-2B, Qwen3.5-9B-Base
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Recent events (5)
SVR: Self-Verifying Refinement enables adaptive test-time compute via joint verdict-confidence RL
Researchers introduce Self-Verifying Refinement (SVR), a multi-turn reinforcement learning framework that trains language models to self-verify their own outputs and adaptively stop refinement without external verifiers. The model produces a solution, a correctness verdict, and a confidence score at each turn, continuing refinement only when uncertain; ground-truth labels are used only for reward construction, not at inference. Trained with GRPO on Qwen3.5-2B, SVR achieves competitive accuracy on seven math reasoning benchmarks using an average of only 2.99 inference turns, outperforming fixed-budget and oracle-guided baselines. The work advances the line of research on efficient, oracle-free test-time compute scaling.
LKValues: First benchmark and instruction corpus for Sri Lankan societal value alignment in LLMs
Researchers introduce LKValues, a resource suite for aligning LLMs with Sri Lankan cultural values, derived from a trilingual survey of 205 respondents. The suite includes LKvaluesIT, a 150k-instance Sinhala-English instruction corpus, and LKvaluesBench, a 1,000-instance evaluation benchmark. Fine-tuning experiments on Qwen and Aya-Expanse models show that current LLMs exhibit cultural and low-resource alignment gaps, and that LKValues fine-tuning reduces invalid outputs and cross-lingual disparities. The work offers a replicable pipeline for country-specific pluralist value alignment in underrepresented languages.
Qwen releases Qwen3.5-9B-Base multimodal model on Hugging Face
Qwen has released Qwen3.5-9B-Base, a 9-billion-parameter image-text-to-text base model on Hugging Face. The model supports conversational use and is compatible with the transformers library and inference endpoints. With over 153,000 downloads, it has seen substantial early adoption.
Qwen releases Qwen3.5-2B multimodal model on Hugging Face
Alibaba's Qwen team released Qwen3.5-2B, a 2-billion-parameter image-text-to-text model, on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With nearly 2 million downloads, it has seen substantial community uptake.
Qwen releases Qwen3.5-2B-Base multimodal model on Hugging Face
Qwen released Qwen3.5-2B-Base, a 2-billion parameter base model supporting image-text-to-text tasks, on Hugging Face. The model is tagged as conversational and endpoints-compatible, suggesting deployment readiness. With nearly 180K downloads, it has seen significant early adoption in the open-weights community.