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[
{
"content": "What is Qwen3?",
"role": "user"
},
{
"content": "Qwen3 is the newest generation of the Qwen model family. It includes a collection of large language models designed to improve performance, efficiency, and multilingual abilities. The series contains both dense models and Mixture-of-Expe... |
[
{
"content": "Can you explain what Qwen3 is?",
"role": "user"
},
{
"content": "Qwen3 is the latest release in the Qwen series of large language models. It focuses on improving reasoning ability, efficiency, and multilingual support. The family includes both dense architectures and Mixture-of-Experts... |
[
{
"content": "What does Qwen3 refer to?",
"role": "user"
},
{
"content": "Qwen3 refers to the newest generation of Qwen large language models developed to enhance performance and multilingual capabilities. The series includes both dense models and MoE architectures with parameter scales ranging from... |
[
{
"content": "Tell me about Qwen3.",
"role": "user"
},
{
"content": "Qwen3 is a family of advanced large language models designed to deliver improved reasoning, efficiency, and multilingual support. It offers both dense and Mixture-of-Experts architectures with model sizes ranging between 0.6 billio... |
[
{
"content": "What are the key innovations in Qwen3?",
"role": "user"
},
{
"content": "Qwen3 introduces several innovations, including the integration of thinking and non-thinking modes in a single framework, a thinking budget system that dynamically allocates computational resources, and strong-to-... |
[
{
"content": "What new features does Qwen3 introduce?",
"role": "user"
},
{
"content": "Some major innovations in Qwen3 include a unified framework that supports both thinking and non-thinking modes, a thinking budget mechanism that controls reasoning computation, and strong-to-weak distillation met... |
[
{
"content": "What makes Qwen3 different from earlier versions?",
"role": "user"
},
{
"content": "Qwen3 differs from earlier versions by introducing a unified architecture combining thinking and non-thinking modes, a flexible thinking budget to optimize inference resources, and advanced strong-to-we... |
[
{
"content": "What are the main improvements in Qwen3?",
"role": "user"
},
{
"content": "The main improvements in Qwen3 include a combined thinking and non-thinking model architecture, a thinking budget system that allows adaptive reasoning computation, and strong-to-weak distillation methods that e... |
[
{
"content": "How many languages does Qwen3 support?",
"role": "user"
},
{
"content": "Qwen3 supports 119 languages and dialects. This is a significant expansion compared to Qwen2.5, which supported only 29 languages.",
"role": "assistant"
}
] |
[
{
"content": "What languages can Qwen3 understand?",
"role": "user"
},
{
"content": "Qwen3 is designed to understand and generate text in 119 languages and dialects, greatly expanding multilingual capabilities compared to earlier Qwen models.",
"role": "assistant"
}
] |
[
{
"content": "How many languages are supported by the Qwen3 model?",
"role": "user"
},
{
"content": "The Qwen3 model supports 119 different languages and dialects, providing improved multilingual understanding and generation compared to Qwen2.5.",
"role": "assistant"
}
] |
[
{
"content": "Does Qwen3 support multiple languages?",
"role": "user"
},
{
"content": "Yes. Qwen3 supports 119 languages and dialects, significantly expanding multilingual capabilities compared with previous versions of the Qwen model family.",
"role": "assistant"
}
] |
[
{
"content": "What is the flagship model in the Qwen3 series?",
"role": "user"
},
{
"content": "The flagship model in the Qwen3 series is Qwen3-235B-A22B, a Mixture-of-Experts model with 235 billion total parameters and 22 billion activated parameters per token.",
"role": "assistant"
}
] |
[
{
"content": "Which model is the main model in Qwen3?",
"role": "user"
},
{
"content": "The primary model in the Qwen3 lineup is Qwen3-235B-A22B. It is an MoE architecture with 235B total parameters and 22B active parameters during inference.",
"role": "assistant"
}
] |
[
{
"content": "What is the most powerful model in the Qwen3 family?",
"role": "user"
},
{
"content": "The most powerful model in the Qwen3 family is Qwen3-235B-A22B, which uses a Mixture-of-Experts architecture with 235 billion total parameters and 22 billion activated parameters.",
"role": "assi... |
[
{
"content": "Which Qwen3 model is considered the flagship?",
"role": "user"
},
{
"content": "Qwen3-235B-A22B is considered the flagship model in the Qwen3 series, featuring a Mixture-of-Experts design with 235B total parameters and 22B active parameters per token.",
"role": "assistant"
}
] |
[
{
"content": "What is the difference between thinking mode and non-thinking mode?",
"role": "user"
},
{
"content": "Thinking mode is designed for complex reasoning tasks that require multiple steps, while non-thinking mode is optimized for faster responses in simpler or conversational tasks.",
"... |
[
{
"content": "How does thinking mode differ from non-thinking mode in Qwen3?",
"role": "user"
},
{
"content": "Thinking mode focuses on deeper reasoning and multi-step problem solving, whereas non-thinking mode prioritizes speed and efficiency for general queries.",
"role": "assistant"
}
] |
[
{
"content": "What are thinking and non-thinking modes in Qwen3?",
"role": "user"
},
{
"content": "Thinking mode is used for tasks that require detailed reasoning and complex analysis, while non-thinking mode is intended for quick, context-based responses.",
"role": "assistant"
}
] |
[
{
"content": "Why does Qwen3 have thinking and non-thinking modes?",
"role": "user"
},
{
"content": "Qwen3 includes thinking and non-thinking modes so users can choose between deeper reasoning for complex problems or faster responses for everyday tasks.",
"role": "assistant"
}
] |
[
{
"content": "What is the thinking budget mechanism?",
"role": "user"
},
{
"content": "The thinking budget mechanism allows users to allocate computational resources adaptively during inference by specifying a token budget for the thinking process. This helps balance latency and reasoning quality de... |
[
{
"content": "How does the thinking budget mechanism work?",
"role": "user"
},
{
"content": "The thinking budget mechanism lets users define how many tokens the model can use for its reasoning process during inference, enabling a trade-off between faster responses and deeper reasoning.",
"role":... |
[
{
"content": "What does the thinking budget mechanism do in Qwen3?",
"role": "user"
},
{
"content": "In Qwen3, the thinking budget mechanism allows dynamic allocation of computation for reasoning by setting a token limit for the thinking stage, improving performance as the thinking budget increases.... |
[
{
"content": "Why is the thinking budget mechanism important?",
"role": "user"
},
{
"content": "The thinking budget mechanism is important because it enables users to control how much computation is spent on reasoning, allowing them to balance response speed with answer quality.",
"role": "assis... |
[
{
"content": "How many dense models are in the Qwen3 series?",
"role": "user"
},
{
"content": "The Qwen3 series includes six dense models: Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen3-14B, and Qwen3-32B.",
"role": "assistant"
}
] |
[
{
"content": "What dense models are included in Qwen3?",
"role": "user"
},
{
"content": "Qwen3 provides six dense models in its lineup: Qwen3-0.6B, 1.7B, 4B, 8B, 14B, and 32B.",
"role": "assistant"
}
] |
[
{
"content": "Which dense models are available in the Qwen3 family?",
"role": "user"
},
{
"content": "The dense model lineup of Qwen3 consists of six models: 0.6B, 1.7B, 4B, 8B, 14B, and 32B parameter versions.",
"role": "assistant"
}
] |
[
{
"content": "How many standard dense models does Qwen3 provide?",
"role": "user"
},
{
"content": "Qwen3 offers six dense models ranging from 0.6B parameters to 32B parameters.",
"role": "assistant"
}
] |
[
{
"content": "How many MoE models are in the Qwen3 series?",
"role": "user"
},
{
"content": "The Qwen3 series includes two Mixture-of-Experts (MoE) models: Qwen3-30B-A3B and Qwen3-235B-A22B.",
"role": "assistant"
}
] |
[
{
"content": "Which MoE models exist in Qwen3?",
"role": "user"
},
{
"content": "Qwen3 provides two MoE models in its lineup: Qwen3-30B-A3B and the larger Qwen3-235B-A22B.",
"role": "assistant"
}
] |
[
{
"content": "What Mixture-of-Experts models are part of Qwen3?",
"role": "user"
},
{
"content": "The Mixture-of-Experts models in the Qwen3 family are Qwen3-30B-A3B and Qwen3-235B-A22B.",
"role": "assistant"
}
] |
[
{
"content": "How many MoE architectures does Qwen3 include?",
"role": "user"
},
{
"content": "Qwen3 includes two MoE architectures: Qwen3-30B-A3B and Qwen3-235B-A22B.",
"role": "assistant"
}
] |
[
{
"content": "What architecture components do Qwen3 dense models use?",
"role": "user"
},
{
"content": "Qwen3 dense models use Grouped Query Attention (GQA), SwiGLU activation, Rotary Positional Embeddings (RoPE), and RMSNorm with pre-normalization.",
"role": "assistant"
}
] |
[
{
"content": "Which architectural techniques are used in Qwen3 dense models?",
"role": "user"
},
{
"content": "Qwen3 dense models incorporate components such as GQA attention, SwiGLU activation functions, RoPE positional embeddings, and RMSNorm for stable training.",
"role": "assistant"
}
] |
[
{
"content": "What technologies power Qwen3 dense model architecture?",
"role": "user"
},
{
"content": "The architecture of Qwen3 dense models includes Grouped Query Attention, SwiGLU activations, Rotary Positional Embeddings, and RMSNorm with pre-normalization.",
"role": "assistant"
}
] |
[
{
"content": "How are Qwen3 dense models architected?",
"role": "user"
},
{
"content": "Qwen3 dense models rely on modern transformer components such as GQA, SwiGLU activation, RoPE positional encoding, and RMSNorm normalization.",
"role": "assistant"
}
] |
[
{
"content": "What is the context length supported by Qwen3 models?",
"role": "user"
},
{
"content": "Qwen3 models support context lengths up to 128K tokens for most models, while smaller models such as 0.6B and 1.7B support 32K tokens.",
"role": "assistant"
}
] |
[
{
"content": "What is the maximum context window for Qwen3?",
"role": "user"
},
{
"content": "Most Qwen3 models support a context window of up to 128K tokens, while the smallest models support 32K tokens.",
"role": "assistant"
}
] |
[
{
"content": "How long of a context can Qwen3 models handle?",
"role": "user"
},
{
"content": "Qwen3 models generally handle up to 128K tokens of context, though smaller models are limited to 32K tokens.",
"role": "assistant"
}
] |
[
{
"content": "What context length capability do Qwen3 models provide?",
"role": "user"
},
{
"content": "The context length of Qwen3 models reaches 128K tokens for most models, while smaller variants support up to 32K tokens.",
"role": "assistant"
}
] |
[
{
"content": "How many tokens was Qwen3 pre-trained on?",
"role": "user"
},
{
"content": "Qwen3 was pre-trained on approximately 36 trillion tokens, carefully curated to ensure linguistic and domain diversity across 119 languages and dialects.",
"role": "assistant"
}
] |
[
{
"content": "How large was the pre-training dataset for Qwen3?",
"role": "user"
},
{
"content": "The Qwen3 models were pre-trained on about 36 trillion tokens collected from diverse domains and languages to improve knowledge coverage and multilingual performance.",
"role": "assistant"
}
] |
[
{
"content": "What is the size of the training corpus used for Qwen3?",
"role": "user"
},
{
"content": "Qwen3 was trained using roughly 36 trillion tokens of curated data spanning many domains and supporting 119 languages and dialects.",
"role": "assistant"
}
] |
[
{
"content": "How many tokens were used to train Qwen3?",
"role": "user"
},
{
"content": "Approximately 36 trillion tokens were used during the pre-training of Qwen3, ensuring broad coverage across multiple languages and subject areas.",
"role": "assistant"
}
] |
[
{
"content": "What are the three pre-training stages for Qwen3?",
"role": "user"
},
{
"content": "The three stages are: General Stage (S1) with over 30 trillion tokens for broad knowledge, Reasoning Stage (S2) with around 5 trillion higher-quality tokens focused on STEM and reasoning tasks, and the ... |
[
{
"content": "How is the Qwen3 pre-training process structured?",
"role": "user"
},
{
"content": "Qwen3 pre-training is divided into three phases: a general training phase with large-scale data, a reasoning-focused phase with high-quality STEM and coding data, and a long-context phase that expands t... |
[
{
"content": "What stages are involved in training Qwen3?",
"role": "user"
},
{
"content": "The training process includes three stages: general pre-training on massive data, a reasoning-focused stage with improved STEM and coding data, and a long-context training stage to increase sequence length ca... |
[
{
"content": "Can you describe the three training phases of Qwen3?",
"role": "user"
},
{
"content": "Qwen3 training includes a general knowledge stage using trillions of tokens, a reasoning stage emphasizing STEM and coding data, and a long-context stage designed to extend sequence length for larger... |
[
{
"content": "What techniques were used to extend Qwen3's context length?",
"role": "user"
},
{
"content": "Qwen3 extended its context length by increasing the base frequency of RoPE to 1,000,000 using the ABF technique and incorporating YARN and Dual Chunk Attention to improve long-sequence handlin... |
[
{
"content": "How did Qwen3 achieve longer context windows?",
"role": "user"
},
{
"content": "Longer context support in Qwen3 was achieved by modifying RoPE frequencies through ABF and integrating techniques like YARN and Dual Chunk Attention for better long-range attention.",
"role": "assistant... |
[
{
"content": "What methods were used to increase Qwen3's sequence length?",
"role": "user"
},
{
"content": "Qwen3 increased sequence length by adjusting RoPE base frequency and using YARN and Dual Chunk Attention to enable larger context windows during inference.",
"role": "assistant"
}
] |
[
{
"content": "How was long-context capability implemented in Qwen3?",
"role": "user"
},
{
"content": "Long-context capability in Qwen3 was implemented by scaling RoPE frequency and introducing techniques such as YARN and Dual Chunk Attention to support longer sequences efficiently.",
"role": "as... |
[
{
"content": "How was synthetic data generated for Qwen3 pre-training?",
"role": "user"
},
{
"content": "Synthetic training data was generated using earlier Qwen models such as Qwen2.5, Qwen2.5-Math, and Qwen2.5-Coder to produce trillions of tokens including textbooks, instructions, question-answeri... |
[
{
"content": "What models were used to create synthetic data for Qwen3?",
"role": "user"
},
{
"content": "Synthetic data for Qwen3 was produced using models like Qwen2.5, Qwen2.5-Math, and Qwen2.5-Coder, generating large-scale text across many domains.",
"role": "assistant"
}
] |
[
{
"content": "How did Qwen3 generate synthetic training datasets?",
"role": "user"
},
{
"content": "Qwen3 synthetic datasets were created with earlier Qwen models that generated large volumes of structured data including educational text, instructions, and programming examples.",
"role": "assist... |
[
{
"content": "Where did the synthetic training data for Qwen3 come from?",
"role": "user"
},
{
"content": "The synthetic training data for Qwen3 was generated using specialized Qwen2.5 models focused on general text, mathematics, and coding tasks.",
"role": "assistant"
}
] |
[
{
"content": "What is the vocabulary size of Qwen3's tokenizer?",
"role": "user"
},
{
"content": "Qwen3 uses a tokenizer with a vocabulary size of 151,669 tokens based on byte-level byte-pair encoding (BBPE).",
"role": "assistant"
}
] |
[
{
"content": "How large is the Qwen3 tokenizer vocabulary?",
"role": "user"
},
{
"content": "The tokenizer used in Qwen3 has a vocabulary size of 151,669 and relies on byte-level BPE encoding.",
"role": "assistant"
}
] |
[
{
"content": "What tokenizer vocabulary size does Qwen3 use?",
"role": "user"
},
{
"content": "Qwen3 employs a tokenizer with 151,669 tokens in its vocabulary using byte-level byte-pair encoding.",
"role": "assistant"
}
] |
[
{
"content": "How many tokens are in Qwen3's tokenizer vocabulary?",
"role": "user"
},
{
"content": "The Qwen3 tokenizer contains a vocabulary of 151,669 tokens and is implemented using byte-level BPE.",
"role": "assistant"
}
] |
[
{
"content": "How many experts do Qwen3 MoE models have?",
"role": "user"
},
{
"content": "Qwen3 MoE models include 128 total experts with 8 experts activated per token. They remove shared experts and use global-batch load balancing to promote expert specialization.",
"role": "assistant"
}
] |
[
{
"content": "How many experts are used in Qwen3 Mixture-of-Experts models?",
"role": "user"
},
{
"content": "The MoE architecture in Qwen3 contains 128 experts in total, with 8 experts activated for each token during inference.",
"role": "assistant"
}
] |
[
{
"content": "What is the expert configuration in Qwen3 MoE models?",
"role": "user"
},
{
"content": "Qwen3 MoE models consist of 128 experts overall, and 8 of them are selected dynamically for each token during computation.",
"role": "assistant"
}
] |
[
{
"content": "How does expert selection work in Qwen3 MoE architecture?",
"role": "user"
},
{
"content": "Qwen3 MoE models contain 128 experts, and only 8 experts are activated per token, allowing efficient computation and specialization.",
"role": "assistant"
}
] |
[
{
"content": "What benchmarks were used to evaluate Qwen3 base models?",
"role": "user"
},
{
"content": "Qwen3 base models were evaluated using 15 benchmarks including tasks in general knowledge, mathematics, coding, and multilingual understanding.",
"role": "assistant"
}
] |
[
{
"content": "Which evaluation benchmarks were used for Qwen3 base models?",
"role": "user"
},
{
"content": "The Qwen3 base models were tested on 15 benchmarks across categories like general reasoning, STEM problems, programming tasks, and multilingual evaluation.",
"role": "assistant"
}
] |
[
{
"content": "How were Qwen3 base models evaluated?",
"role": "user"
},
{
"content": "Evaluation of Qwen3 base models used 15 benchmarks covering general tasks such as MMLU, math datasets like GSM8K, coding tests like MBPP, and multilingual benchmarks.",
"role": "assistant"
}
] |
[
{
"content": "What datasets or benchmarks measure Qwen3 performance?",
"role": "user"
},
{
"content": "Qwen3 base models were tested on 15 benchmarks including MMLU, BBH, GPQA, GSM8K, MATH, EvalPlus, MultiPL-E, MBPP, CRUX-O, MGSM, MMMLU, and INCLUDE.",
"role": "assistant"
}
] |
[
{
"content": "How does Qwen3-235B-A22B-Base compare to DeepSeek-V3-Base?",
"role": "user"
},
{
"content": "Qwen3-235B-A22B-Base surpasses DeepSeek-V3-Base on 14 of 15 benchmarks while using significantly fewer parameters and lower inference cost.",
"role": "assistant"
}
] |
[
{
"content": "How does Qwen3 flagship base model perform against DeepSeek-V3-Base?",
"role": "user"
},
{
"content": "The Qwen3-235B-A22B-Base model outperforms DeepSeek-V3-Base on most evaluation benchmarks while using fewer total and activated parameters.",
"role": "assistant"
}
] |
[
{
"content": "What is the performance comparison between Qwen3-235B-A22B-Base and DeepSeek-V3-Base?",
"role": "user"
},
{
"content": "Qwen3-235B-A22B-Base achieves better results on 14 out of 15 benchmarks while requiring fewer computational resources compared with DeepSeek-V3-Base.",
"role": "a... |
[
{
"content": "Is Qwen3-235B-A22B-Base stronger than DeepSeek-V3-Base?",
"role": "user"
},
{
"content": "Yes, Qwen3-235B-A22B-Base performs better on the majority of evaluation benchmarks while using fewer parameters, making it more efficient.",
"role": "assistant"
}
] |
[
{
"content": "What is the post-training pipeline for Qwen3 flagship models?",
"role": "user"
},
{
"content": "The pipeline has four stages: Long-CoT Cold Start, Reasoning Reinforcement Learning, Thinking Mode Fusion, and General Reinforcement Learning.",
"role": "assistant"
}
] |
[
{
"content": "How are Qwen3 flagship models post-trained?",
"role": "user"
},
{
"content": "Post-training involves four phases including cold-start reasoning training, reasoning reinforcement learning, fusion of thinking and non-thinking modes, and a general RL stage.",
"role": "assistant"
}
] |
[
{
"content": "What steps are included in Qwen3 post-training?",
"role": "user"
},
{
"content": "The process includes long chain-of-thought initialization, reinforcement learning for reasoning, mode fusion for flexible responses, and a final RL stage for general performance.",
"role": "assistant"... |
[
{
"content": "Can you explain the Qwen3 post-training stages?",
"role": "user"
},
{
"content": "Qwen3 uses a four-stage post-training pipeline: initializing reasoning patterns, applying reasoning RL, merging thinking modes, and optimizing with general reinforcement learning.",
"role": "assistant... |
[
{
"content": "What is Strong-to-Weak Distillation?",
"role": "user"
},
{
"content": "Strong-to-Weak Distillation is a method where smaller models learn from larger teacher models to improve efficiency while maintaining strong performance.",
"role": "assistant"
}
] |
[
{
"content": "How does Strong-to-Weak Distillation work?",
"role": "user"
},
{
"content": "It transfers knowledge from large models to smaller ones using techniques like off-policy and on-policy distillation to maintain strong performance.",
"role": "assistant"
}
] |
[
{
"content": "What is the purpose of Strong-to-Weak Distillation in Qwen3?",
"role": "user"
},
{
"content": "The goal is to train lightweight models using knowledge from stronger models, reducing training cost while preserving high capability.",
"role": "assistant"
}
] |
[
{
"content": "Why is Strong-to-Weak Distillation used?",
"role": "user"
},
{
"content": "It helps smaller models achieve strong performance by learning from large teacher models while significantly reducing computational cost.",
"role": "assistant"
}
] |
[
{
"content": "How does Qwen3 handle mode switching in chat templates?",
"role": "user"
},
{
"content": "Qwen3 uses /think and /no_think flags in user queries or system messages to switch between thinking and non-thinking modes.",
"role": "assistant"
}
] |
[
{
"content": "How can users switch thinking modes in Qwen3 chat templates?",
"role": "user"
},
{
"content": "Users can control reasoning modes using flags like /think or /no_think in the prompt or system message.",
"role": "assistant"
}
] |
[
{
"content": "What mechanism controls thinking mode in Qwen3 prompts?",
"role": "user"
},
{
"content": "Mode switching is handled with special flags that instruct the model to either produce reasoning traces or respond directly.",
"role": "assistant"
}
] |
[
{
"content": "How do prompts enable thinking or non-thinking responses in Qwen3?",
"role": "user"
},
{
"content": "Special prompt flags such as /think or /no_think allow users to control whether the model performs extended reasoning.",
"role": "assistant"
}
] |
[
{
"content": "What happens when the thinking budget is exceeded?",
"role": "user"
},
{
"content": "When the thinking budget is reached, the reasoning process stops and the model generates a final answer based on the reasoning already produced.",
"role": "assistant"
}
] |
[
{
"content": "What occurs if Qwen3 exceeds the thinking token limit?",
"role": "user"
},
{
"content": "If the reasoning token limit is exceeded, the system halts further thinking and proceeds directly to producing the final response.",
"role": "assistant"
}
] |
[
{
"content": "How does Qwen3 react when the thinking limit is reached?",
"role": "user"
},
{
"content": "Once the thinking budget threshold is reached, the model stops reasoning and outputs the final answer using the accumulated analysis.",
"role": "assistant"
}
] |
[
{
"content": "What is the behavior of Qwen3 after thinking tokens run out?",
"role": "user"
},
{
"content": "After the token limit for thinking is reached, the model switches to generating the final response.",
"role": "assistant"
}
] |
[
{
"content": "What types of rewards are used in General RL stage?",
"role": "user"
},
{
"content": "Three reward types are used: rule-based rewards, model-based rewards with reference answers, and model-based rewards trained from human preference data.",
"role": "assistant"
}
] |
[
{
"content": "What reward mechanisms are used during the General RL stage?",
"role": "user"
},
{
"content": "The General RL stage uses rule-based evaluation, model-based scoring with references, and human preference-based rewards.",
"role": "assistant"
}
] |
[
{
"content": "How are rewards structured in Qwen3 general reinforcement learning?",
"role": "user"
},
{
"content": "Rewards include strict rule-based checks, model evaluations using reference answers, and learned reward models trained from human feedback.",
"role": "assistant"
}
] |
[
{
"content": "What reward systems guide Qwen3 General RL training?",
"role": "user"
},
{
"content": "The RL process uses rule-based scoring, reference-based evaluation models, and human preference reward models.",
"role": "assistant"
}
] |
[
{
"content": "What capabilities does General RL target?",
"role": "user"
},
{
"content": "General RL focuses on instruction following, format compliance, alignment with human preferences, tool usage ability, and specialized tasks like RAG.",
"role": "assistant"
}
] |
[
{
"content": "What skills are improved by the General RL stage?",
"role": "user"
},
{
"content": "The stage enhances instruction adherence, formatting accuracy, preference alignment, tool usage skills, and specialized task abilities.",
"role": "assistant"
}
] |
[
{
"content": "What abilities does Qwen3 improve through General RL?",
"role": "user"
},
{
"content": "It improves instruction following, formatting, alignment with human feedback, agent capabilities, and performance in specialized scenarios.",
"role": "assistant"
}
] |
[
{
"content": "What core capabilities does General RL train in Qwen3?",
"role": "user"
},
{
"content": "The training targets instruction compliance, formatting consistency, alignment with preferences, tool invocation, and advanced application scenarios.",
"role": "assistant"
}
] |
[
{
"content": "What sampling parameters are used for Qwen3 in thinking mode?",
"role": "user"
},
{
"content": "Thinking mode typically uses temperature 0.6, top-p 0.95, and top-k 20 with a maximum output length of around 32K tokens.",
"role": "assistant"
}
] |
[
{
"content": "What are the decoding parameters for Qwen3 thinking mode?",
"role": "user"
},
{
"content": "The recommended parameters are temperature=0.6, top-p=0.95, and top-k=20 with long output limits.",
"role": "assistant"
}
] |
[
{
"content": "How is sampling configured for Qwen3 reasoning mode?",
"role": "user"
},
{
"content": "Sampling uses moderate randomness with temperature 0.6, nucleus sampling top-p 0.95, and top-k 20.",
"role": "assistant"
}
] |
[
{
"content": "What generation settings are used in Qwen3 thinking mode?",
"role": "user"
},
{
"content": "Typical settings include temperature 0.6, top-p 0.95, top-k 20, and long output limits up to tens of thousands of tokens.",
"role": "assistant"
}
] |
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