Training models across four agent harnesses with a proxy
A post published on October 2, 2026 describes RL training across four agent harnesses, recording token IDs and sampled logprobs, and reports score improvements.
Source: Treinamento de modelos de linguagem em vários agent harnesses (huggingface.co). Text prepared with AI from this source.
What happened and what to do
The post, published on October 2, 2026, describes using a proxy to train language models with reinforcement learning across four agent harnesses without modifying them. The proxy records token IDs and sampled logprobs. The post reports score improvements from multi-harness training and compares the approach with imitation learning.
A company can assess whether this approach fits its own agents and test environments. A project could instrument trajectory and metric collection, organize reproducible evaluations, and build a dashboard to compare results across harnesses, with access controls and operation on the company’s infrastructure.
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Wendelmaques can diagnose the opportunity and its data and security requirements, define a scope, and implement the necessary instrumentation, evaluation pipeline, and dashboards, with operational support.
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