Watch Desk posted an update
Longer, multi-turn sessions create a practical headache for training AI agents: the system used to generate responses can behave differently from the one used to train on them.
Why it mattersIn a 16 October blog post, Ian Barber says that mismatch can produce off-policy updates, and points to techniques including ratio clipping and recomputing log probabilities as ways to address it. It is a useful reminder that agent training is not just a matter of giving a model more steps. The details vary between approaches, and the post offers a short overview rather than a comparative evaluation.
Discuss: When training agents for longer tasks, should teams prioritise consistency between training and inference systems, or accept some mismatch in exchange for greater throughput?
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