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In September 2024, OpenAI introduced its o1 model, marking the beginning of a significant shift in the AI landscape. This transformation accelerated with the release of DeepSeek R1 in January 2025, igniting a competitive race among major AI model developers to improve reasoning capabilities in language models. These new models are designed to provide thoughtful, well-reasoned responses to queries, emphasizing a “chain-of-thought” approach where the models reflect on their conclusions and validate them before responding.
ByteDance, the parent company of TikTok, has entered this competitive sphere with its recent announcement and the release of a technical paper detailing the underlying technology of Seed-Thinking-v1.5. This upcoming large language model (LLM) aims to enhance reasoning abilities across STEM fields and general domains.
While Seed-Thinking-v1.5 has not yet been made available for public use, details shared in the technical paper provide valuable insights into its potential impact upon release.
Built atop the increasingly popular Mixture-of-Experts (MoE) architecture
Similar to Meta’s Llama 4 and Mistral’s Mixtral, Seed-Thinking-v1.5 utilizes a Mixture-of-Experts (MoE) architecture, which enhances model efficiency by integrating various specialized models into a cohesive system. In this context, Seed-Thinking-v1.5 activates only 20 billion parameters at a time from a total of 200 billion, aiming to prioritize structured reasoning.
According to ByteDance’s technical paper, the model exhibits superior performance on reasoning tasks, reportedly surpassing DeepSeek R1 and nearing the capabilities of Google’s Gemini 2.5 Pro and OpenAI’s o3-mini-high reasoner in third-party evaluations. Notably, it outperformed its counterparts on the ARC-AGI benchmark, a metric that tracks advancements towards artificial general intelligence.
Performance benchmarks and model focus
Seed-Thinking-v1.5 has shown promising results across a variety of tests, achieving an 86.7% score on AIME 2024, a 55.0% pass rate at 8 on Codeforces, and a 77.3% score on the GPQA science benchmark. These outcomes indicate that it rivals models like OpenAI’s o3-mini-high and Google’s Gemini 2.5 Pro in specific reasoning assessments.
Beyond reasoning tasks, the model’s strength is evident in human preference evaluations, where it secured an 8.0% higher win rate compared to DeepSeek R1. This suggests that its capabilities are not limited to logical reasoning or mathematical challenges alone.
To refine benchmark assessments, ByteDance has introduced BeyondAIME, a more intricate math benchmark designed to provide genuine challenges for models and discourage reliance on memorization. This new evaluation tool is expected to be publicly accessible alongside additional resources such as the Codeforces dataset.
Data strategy
The development of Seed-Thinking-v1.5 has been deeply influenced by its training data. For supervised fine-tuning (SFT), the team assembled 400,000 samples, with 300,000 dedicated to verifiable tasks in STEM, coding, and logic, while the remaining 100,000 encompassed non-verifiable challenges, including creative writing and role-playing.
For reinforcement learning, the data was categorized into:
- Verifiable Problems: 100,000 rigorously selected STEM questions and logic puzzles with established answers.
- Non-Verifiable Tasks: Datasets featuring human preferences geared towards open-ended prompts.
The STEM-related data predominantly emphasizes advanced mathematical challenges, making up over 80% of the problem collection, which also includes variants of tasks like Sudoku, adjusted for difficulty based on model performance.
Reinforcement learning approach
The reinforcement learning framework utilized by Seed-Thinking-v1.5 employs custom actor-critic (VAPO) and policy-gradient (DAPO) structures to enhance training stability and address previously identified issues in RL training. These strategies are designed to mitigate reward signal sparsity, particularly within long chain-of-thought settings.
Reward models, essential for supervising RL outputs, include two innovative tools developed by ByteDance:
- Seed-Verifier: A rule-based LLM assessing mathematical equivalence between generated and reference answers.
- Seed-Thinking-Verifier: A step-by-step reasoning judge designed to ensure consistency and resist manipulation of reward evaluations.
This dual-layer reward system allows for nuanced performance assessment across a spectrum of tasks.
Infrastructure and scaling
ByteDance’s training efficiency is supported by a system built on the HybridFlow framework, leveraging Ray clusters to manage operations effectively and minimize GPU idle time. A significant advancement is the Streaming Rollout System (SRS), which enhances the speed of iterations by managing incomplete model generations across different versions concurrently, resulting in reported RL cycle speeds of up to three times faster.
Additional infrastructure optimizations include:
- Memory-efficient mixed precision (FP8)
- Expert parallelism combined with kernel auto-tuning for better MoE efficiency
- ByteCheckpoint for reliable checkpointing
- AutoTuner aimed at optimizing memory configurations and parallelism
Human evaluation and real-world impact
ByteDance assessed the model’s alignment with human expectations through tests in creative writing, humanities knowledge, and everyday conversation. Across multiple evaluation sessions, Seed-Thinking-v1.5 consistently bested DeepSeek R1, proving its effectiveness in catering to user demands.
The developers observed that the strengths of reasoning models trained on verifiable tasks translate effectively to creative domains, attributed to the rigorous structure inherent in their mathematical training methodologies.
What it means for technical leaders, data engineers and enterprise decision-makers
For professionals overseeing the lifecycle of LLMs—from data collection to deployment—Seed-Thinking-v1.5 represents a new paradigm for integrating reasoning functionalities within enterprise AI ecosystems. Its modular training approach, characterized by the inclusion of verifiable reasoning datasets and a multi-phase reinforcement learning process, is particularly relevant for teams aiming to scale LLM development while ensuring granular oversight.
ByteDance’s incorporation of Seed-Verifier and Seed-Thinking-Verifier introduces a pathway for more reliable reward modeling, crucial for applications in customer-facing or regulated environments. This could enable teams working under pressure to benefit from the model’s stability in reinforcement learning processes, thanks to innovations like VAPO and dynamic sampling, which promise reduced iteration times.
From a deployment perspective, the hybrid infrastructure, featuring the SRS and FP8 optimization, points to enhanced training efficiency and improved hardware utilization. This advancement will be advantageous for engineers tasked with expanding LLM operations across both cloud and on-premises environments.
For those responsible for ensuring consistency and reliability, the design principles behind Seed-Thinking-v1.5 can serve as a foundational template for developing resilient and multi-faceted orchestration systems.
For data engineering specialists, the structured focus on training datasets—including meticulous filtering, augmentation, and expert verification—highlights the critical role of data quality in enhancing model performance. This aspect could motivate teams to take a more principled approach to the creation and validation of data pipelines.
Future outlook
Seed-Thinking-v1.5 is a collaborative effort from ByteDance’s Seed LLM Systems team, guided by Yonghui Wu and featuring public representation by Haibin Lin, a veteran in AI research. This project also incorporates insights and methodologies from earlier initiatives like Doubao 1.5 Pro.
Looking ahead, the team is committed to further refining its reinforcement learning strategies, aiming to enhance both training efficiency and reward modeling techniques for non-verifiable tasks, while the forthcoming public release of benchmarks such as BeyondAIME is expected to stimulate further advancements in reasoning-driven AI research.
Source
venturebeat.com