Training language models across multiple agent harnesses
A guide describes a proxy that records token IDs and sampled logprobs to train models with RL across four agent harnesses, with reported score gains and a comparison with imitation learning.
According to the briefing, a guide published by an open model platform describes using a proxy to train language models with reinforcement learning (RL) across four agent harnesses. The proxy records the sampled token IDs and logprobs during runs. The post reports score improvements from multi-harness training and compares the method with imitation learning. The briefing is dated October 2, 2026.
To consult the original source, search for the guide by its title and the platform name on that platform's official channels, and check the score tables, the proxy setup and the benchmarks used before accepting any conclusion. The relevance for engineers lies in the idea of training models across different harnesses without modifying them. Rota Nacional does not offer model training or RL fine-tuning. If you use AI to study this material, do not paste code, logs or excerpts containing personal data or credentials into the chat.