Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2609.22194.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Paper on balancing SFT and RL for enterprise analytics agents. Proposes a diagnostic routing framework categorizing features into Imitation, Lift, and Discovery regimes to select training strategy. On GPT-OSS 120B, targeted SFT+RL improved 7/8 advertiser skills, reduced standard leakage from 11.8% to 2.9%, and used 43% less RL compute than uniform application.

Concrete diagnostic for deciding when RL helps versus perturbs SFT-calibrated agent skills, with measured compute savings and leakage drops. Useful for teams training long-horizon tool-use agents.

Cited by

No citations on record.