Alibaba Qwen QwQ-32B: Scaled reinforcement learning showcase

The Qwen workforce at Alibaba has unveiled QwQ-32B, a 32 billion parameter AI fashion that demonstrates efficiency rivalling the a lot higher DeepSeek-R1. This step forward highlights the potential for scaling Reinforcement Studying (RL) on powerful basis fashions.

The Qwen workforce have effectively built-in agent features into the reasoning fashion, enabling it to suppose seriously, utilise equipment, and adapt its reasoning in response to environmental comments.

“Scaling RL has the possible to toughen fashion efficiency past typical pretraining and post-training strategies,” the workforce mentioned. “Fresh research have demonstrated that RL can considerably make stronger the reasoning features of fashions.”

QwQ-32B achieves efficiency similar to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated), a testomony to the effectiveness of RL when carried out to powerful basis fashions pretrained on intensive international wisdom. This exceptional final results underscores the potential for RL to bridge the space between fashion dimension and function.

The fashion has been evaluated throughout a variety of benchmarks, together with AIME24, LiveCodeBench, LiveBench, IFEval, and BFCL, designed to evaluate its mathematical reasoning, coding skillability, and common problem-solving features.

The effects spotlight QwQ-32B’s efficiency compared to different main fashions, together with DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the unique DeepSeek-R1.

Benchmark effects:

• AIME24: QwQ-32B accomplished 79.5, somewhat at the back of DeepSeek-R1-6718’s 79.8, however considerably forward of OpenAl-o1-mini’s 63.6 and the distilled fashions.
• LiveCodeBench: QwQ-32B scored 63.4, once more intently matched through DeepSeek-R1-6718’s 65.9, and surpassing the distilled fashions and OpenAl-o1-mini’s 53.8.
• LiveBench: QwQ-32B accomplished 73.1, with DeepSeek-R1-6718 scoring 71.6, and outperforming the distilled fashions and OpenAl-o1-mini’s 57.5.
• IFEval: QwQ-32B scored 83.9, very on the subject of DeepSeek-R1-6718’s 83.3, and main the distilled fashions and OpenAl-o1-mini’s 59.1.
• BFCL: QwQ-32B accomplished 66.4, with DeepSeek-R1-6718 scoring 62.8, demonstrating a lead over the distilled fashions and OpenAl-o1-mini’s 49.3.

The Qwen workforce’s manner concerned a cold-start checkpoint and a multi-stage RL procedure pushed through outcome-based rewards. The preliminary level concerned with scaling RL for math and coding duties, utilising accuracy verifiers and code execution servers. The second one level expanded to common features, incorporating rewards from common praise fashions and rule-based verifiers.

“We discover that this level of RL practicing with a small quantity of steps can building up the efficiency of alternative common features, akin to instruction following, alignment with human choice, and agent efficiency, with out vital efficiency drop in math and coding,” the workforce defined.

QwQ-32B is open-weight and to be had on Hugging Face and ModelScope beneath the Apache 2.0 license, and could also be out there by means of Qwen Chat. The Qwen workforce perspectives this as an preliminary step in scaling RL to toughen reasoning features and goals to additional discover the mixing of brokers with RL for long-horizon reasoning.

“As we paintings in opposition to growing the following technology of Qwen, we’re assured that combining more potent basis fashions with RL powered through scaled computational assets will propel us nearer to reaching Synthetic Normal Intelligence (AGI),” the workforce mentioned.

See additionally: Deepgram Nova-3 Clinical: AI speech fashion cuts healthcare transcription mistakes

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