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Baidu Launches Ernie 5.1, Slashing Pre-training Costs by 94% and Ranking Among Top Four Globally
Baidu officially launched its next-generation language model, Ernie 5.1, on May 11, 2026. Building upon the Ernie 5.0 pre-training foundation released earlier in January—a model with 2.4 trillion parameters—this new version was developed using an innovative "one-shot elastic training framework." This approach enabled Baidu to optimize multiple model sizes in a single training cycle, slashing Ernie 5.1's pre-training costs to just 6% of comparable models.
As of May 9, Ernie 5.1 held the fourth position globally and ranked first among Chinese models on the Arena Search leaderboard with a score of 1223 points, showcasing its exceptional efficiency and well-balanced performance.
Architecturally, Ernie 5.1 features a sub-model design with tunable depth, width, and number of active experts. Its total parameter count is only one-third of its predecessor, and the number of effective parameters activated per query has been roughly halved. To address the "see-saw effect" common in multi-skill training, Baidu implemented a four-phase post-training process. This method utilizes parallel specialized training code, inference, and proxy expert models, combined with strategy distillation and reinforcement learning, effectively resolving the industry-wide challenge of balancing programming capability with creative reasoning. Furthermore, a revamped reinforcement learning infrastructure decouples model updates, response generation, and evaluation. When paired with a standardized low-precision computation library, this significantly enhances the stability of large-scale training.
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Baidu officially launched its next-generation language model, Ernie 5.1, on May 11, 2026. Building upon the Ernie 5.0 pre-training foundation released earlier in January—a model with 2.4 trillion parameters—this new version was developed using an innovative "one-shot elastic training framework." This approach enabled Baidu to optimize multiple model sizes in a single training cycle, slashing Ernie 5.1's pre-training costs to just 6% of comparable models.
As of May 9, Ernie 5.1 held the fourth position globally and ranked first among Chinese models on the Arena Search leaderboard with a score of 1223 points, showcasing its exceptional efficiency and well-balanced performance.
Architecturally, Ernie 5.1 features a sub-model design with tunable depth, width, and number of active experts. Its total parameter count is only one-third of its predecessor, and the number of effective parameters activated per query has been roughly halved. To address the "see-saw effect" common in multi-skill training, Baidu implemented a four-phase post-training process. This method utilizes parallel specialized training code, inference, and proxy expert models, combined with strategy distillation and reinforcement learning, effectively resolving the industry-wide challenge of balancing programming capability with creative reasoning. Furthermore, a revamped reinforcement learning infrastructure decouples model updates, response generation, and evaluation. When paired with a standardized low-precision computation library, this significantly enhances the stability of large-scale training.
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