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It began with the announcement of OpenAI’s o1 mannequin in Sept. 2024, however actually took off with the DeepSeek R1 launch in Jan. 2025.
Now, it appears that evidently most main AI mannequin suppliers and trainers are in a brand new race to ship higher, quicker, and cheaper “reasoning” AI language fashions — that’s, ones that possibly take a bit longer to reply to a human person, however ideally achieve this with higher, extra complete, extra effectively “reasoned” solutions, which these class of fashions get by performing “chain-of-thought,” reflecting on their very own conclusions and interrogating them for veracity earlier than responding.
ByteDance, the Chinese language internet media large father or mother of TikTok, is the newest to hitch the occasion with the announcement and publication of the technical paper behind Seed-Considering-v1.5, an upcoming giant language mannequin (LLM) designed to advance reasoning efficiency throughout each science, tech, math, and engineering (STEM) fields and general-purpose domains.
The mannequin is just not but accessible for obtain or use, and it’s unclear what the licensing phrases might be—whether or not it will likely be proprietary/closed supply, open supply/free for all to make use of and modify at will, or someplace in between. Nevertheless, the technical paper offers some noteworthy particulars which can be price going over now and prematurely of each time they’re made accessible.
Constructed atop the more and more common Combination-of-Consultants (MoE) structure
Like Meta’s new Llama 4 and Mistral’s Mixtral earlier than it, Seed-Considering-v1.5 is constructed utilizing a Combination-of-Consultants (MoE) structure.
This structure is designed to make fashions extra environment friendly. It primarily combines the capabilities of a number of fashions into one, every specializing in a special area.
On this case, the MoE structure signifies that Seed-Considering-v1.5 makes use of solely 20 billion of the 200 billion parameters at a time.
ByteDance says in its technical paper published to GitHub that Seed-Considering-v1.5 prioritizes structured reasoning and considerate response era.
The outcomes almost converse for themselves, with Seed-Considering-v1.5 outperforming DeepSeek R1 and approaching Google’s newly launched Gemini 2.5 Professional and OpenAI’s o3-mini-high reasoner on many third-party benchmark evaluations. It even exceeds these two within the case of the ARC-AGI benchmark, which measures progress in the direction of synthetic normal intelligence, seen because the objective or “Holy Grail” of AI. This mannequin outperforms people on most economically useful duties, in line with OpenAI’s definition.
Positioned as a compact but succesful different to bigger state-of-the-art fashions, Seed-Considering-v1.5 achieves aggressive benchmark outcomes. It introduces reinforcement studying (RL) improvements, coaching knowledge curation and AI infrastructure.
Efficiency benchmarks and mannequin focus
Seed-Considering-v1.5 reveals robust efficiency on a set of difficult duties, scoring 86.7% on AIME 2024, 55.0% move@8 on Codeforces and 77.3% on the GPQA science benchmark. These outcomes place it near or matching fashions like OpenAI’s o3-mini-high and Google’s Gemini 2.5 Professional on particular reasoning metrics.
On non-reasoning duties, the mannequin was evaluated by way of human choice comparisons and achieved an 8.0% larger win fee over DeepSeek R1, suggesting that its strengths generalize past logic or math-heavy challenges.
To deal with saturation in normal benchmarks like AIME, ByteDance launched BeyondAIME, a brand new, tougher math benchmark with curated issues designed to withstand memorization and higher discriminate mannequin efficiency. This and the Codeforces analysis set are anticipated to be publicly launched to help future analysis.
Information technique
Coaching knowledge performed a central function within the mannequin’s improvement. For supervised fine-tuning (SFT), the crew curated 400,000 samples, together with 300,000 verifiable (STEM, logic and coding duties) and 100,000 non-verifiable issues like inventive writing and role-playing.
For RL coaching, knowledge was segmented into:
- Verifiable issues: 100,000 rigorously filtered STEM questions and logic puzzles with identified solutions, sourced from elite competitions and skilled assessment.
- Non-verifiable duties: Human-preference datasets centered on open-ended prompts, evaluated utilizing pairwise reward fashions.
The STEM knowledge leaned closely on superior arithmetic, accounting for over 80% of the issue set. Extra logic knowledge included duties like Sudoku and 24-point puzzles, with adjustable issue to match mannequin progress.
Reinforcement studying method
Reinforcement studying in Seed-Considering-v1.5 is powered by customized actor-critic (VAPO) and policy-gradient (DAPO) frameworks, developed to handle identified instabilities in RL coaching. These methods cut back reward sign sparsity and improve coaching stability, particularly in lengthy chain-of-thought (CoT) settings.
Reward fashions play a crucial function in supervising RL outputs. ByteDance launched two key instruments:
- Seed-Verifier: A rule-based LLM that checks if generated and reference solutions are mathematically equal.
- Seed-Considering-Verifier: A step-by-step reasoning-based decide that improves judgment consistency and resists reward hacking.
This two-tiered reward system allows nuanced analysis for each easy and sophisticated duties.
Infrastructure and scaling
To help environment friendly large-scale coaching, ByteDance constructed a system atop its HybridFlow framework. Execution is dealt with by Ray clusters, and coaching and inference processes are co-located to scale back GPU idle time.
The Streaming Rollout System (SRS) is a notable innovation that separates mannequin evolution from runtime execution. It accelerates iteration pace by asynchronously managing partially accomplished generations throughout mannequin variations. This structure reportedly delivers as much as 3× quicker RL cycles.
Extra infrastructure methods embody:
- Combined precision (FP8) for reminiscence financial savings
- Professional parallelism and kernel auto-tuning for MoE effectivity
- ByteCheckpoint for resilient and versatile checkpointing
- AutoTuner for optimizing parallelism and reminiscence configurations
Human analysis and real-world influence
To guage alignment with human-centric preferences, ByteDance performed human testing throughout a spread of domains, together with inventive writing, humanities data and normal dialog.
Seed-Considering-v1.5 constantly outperformed DeepSeek R1 throughout periods, reinforcing its applicability to real-world person wants.
The event crew notes that reasoning fashions skilled totally on verifiable duties demonstrated robust generalization to inventive domains—an end result attributed to the construction and rigor embedded in mathematical coaching workflows.
What it means for technical leaders, knowledge engineers and enterprise decision-makers
For technical leads managing the lifecycle of huge language fashions—from knowledge curation to deployment—Seed-Considering-v1.5 presents a chance to rethink how reasoning capabilities are built-in into enterprise AI stacks.
Its modular coaching course of, which incorporates verifiable reasoning datasets and multi-phase reinforcement studying, notably appeals to groups seeking to scale LLM improvement whereas retaining fine-grained management.
ByteDance’s strikes to introduce Seed-Verifier and Seed-Considering-Verifier provide mechanisms for extra reliable reward modeling, which might be crucial when deploying fashions into customer-facing or regulated environments.
For groups working underneath tight deadlines and restricted bandwidth, the mannequin’s stability underneath reinforcement studying, enabled by improvements like VAPO and dynamic sampling, might cut back iteration cycles and streamline fine-tuning for particular duties.
From an orchestration and deployment perspective, the mannequin’s hybrid infrastructure method—together with the Streaming Rollout System (SRS) and help for FP8 optimization—suggests vital positive aspects in coaching throughput and {hardware} utilization.
These options can be useful for engineers liable for scaling LLM operations throughout cloud and on-prem techniques. The truth that Seed-Considering-v1.5 was skilled with mechanisms to adapt reward suggestions primarily based on runtime dynamics speaks on to the challenges of managing heterogeneous knowledge pipelines and sustaining consistency throughout domains.
For groups tasked with making certain reliability, reproducibility, and steady integration of latest instruments, Seed-Considering-v1.5’s system-level design might function a blueprint for constructing strong, multi-modal orchestration techniques.
For knowledge engineering professionals, the structured method to coaching knowledge—together with rigorous filtering, augmentation and skilled verification—reinforces the significance of information high quality as a multiplier of mannequin efficiency. This might encourage extra deliberate approaches to dataset improvement and validation pipelines.
Future outlook
Seed-Considering-v1.5 outcomes from collaboration inside ByteDance’s Seed LLM Techniques crew, led by Yonghui Wu and with public illustration by Haibin Lin, a long-time AI contributor.
The mission additionally attracts on earlier efforts, similar to Doubao 1.5 Professional, and incorporates shared methods in RLHF and knowledge curation.
The crew plans to proceed refining reinforcement studying methods, specializing in coaching effectivity and reward modeling for non-verifiable duties. The general public launch of inside benchmarks similar to BeyondAIME is meant to foster broader development in reasoning-focused AI analysis.
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