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Life sciences · Preprint
arXiv · September 4, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes Persistent Teacher Anchoring (PTA), a method for distillation of language model agents that verifies tool-use calls at the turn level before execution, and reports improvements over on-policy knowledge distillation in two retrieval and perception tasks. The work is methodological and has not been peer reviewed; the magnitude and consistency of the reported gains require independent replication.
Comparative method evaluation on two RL task domains. Student-teacher LLM agent pairs performing tool-use tasks in retrieval and perception environments. Intervention: Persistent Teacher Anchoring (PTA): student-induced, teacher-committed rollout construction with chunk-level verification and turn-level commitment, optionally enhanced with persistent lookahead. Compared with: On-policy knowledge distillation (OPKD).
PTA improves macro best@4 by 2.5 points over OPKD on Search-R1-style retrieval RL under the same downstream budget PTA improves macro best@4 by 2.8 points over OPKD on DeepEyes-style perception RL under the same downstream budget Persistent lookahead improves throughput by 24% compared to standard OPKD
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This is an unrefereed arXiv preprint describing a novel method for training language model agents; it reports improvements over a baseline in a controlled setting but has not undergone peer review.
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Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-generated trajectories to prepare the student for downstream RL. At each state, the student matches a next-token distribution supplied by the teacher. As the rollout enters states the teacher would not visit, the teacher-student distribution gap can accumulate. In tool use, this gap becomes consequential because student-written calls execute before supervision and their observations shape later prefixes. Proposer-verifier generation addresses this drift by letting the teacher decide which student-proposed text is retained during generation. Existing formulations govern text but leave tool execution outside their scope. We propose Persistent Teacher Anchoring (PTA), a student-induced but teacher-committed rollout construction. PTA retains chunk-level verification and adds turn-level commitment, allowing a call to reach the environment only after the teacher has verified the entire turn. Treating verified chunks as atomic generation units, we introduce persistent lookahead, which fills idle rollout capacity by advancing future samples and carrying unfinished ones across student updates under the fixed verifier. Across Search-R1-style retrieval and DeepEyes-style perception RL, applying PTA before downstream RL improves macro best@4 by 2.5 and 2.8 points over OPKD under the same downstream RL budget, while lookahead improves throughput by 24%.
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