@inproceedings{li-etal-2026-rollouts,
title = "From Rollouts to Recipes: Self-Contained Post-Training for {LLM}s",
author = "Li, Yifei and
Zhang, Lingling and
Huang, Muye and
Ma, Zihan and
Liu, Jiashuai and
Liu, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.464/",
pages = "10640--10654",
ISBN = "979-8-89176-459-0",
abstract = "Post-training large language models usually apply a single training recipe to all samples, even though the model{'}s own rollouts reveal different sample-level learning states. We propose Self-Routing, a behavior-conditioned post-training framework that uses rollout correctness and confidence to decide how each sample should be optimized. Depending on its behavior state, a sample is routed to GRPO, on-policy self-distillation, regularization, or skipping, allowing training to adapt without external teachers, extra annotations, or additional sampling. Experiments on mathematical reasoning across Qwen3 and Qwen3.5 backbones show that Self-Routing consistently improves over uniform GRPO, uniform OPSD, fixed mixtures, and simpler routing baselines. Further analyses show that the routing distribution changes over training and reduces unnecessary updates on low-signal or already stable samples."
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<abstract>Post-training large language models usually apply a single training recipe to all samples, even though the model’s own rollouts reveal different sample-level learning states. We propose Self-Routing, a behavior-conditioned post-training framework that uses rollout correctness and confidence to decide how each sample should be optimized. Depending on its behavior state, a sample is routed to GRPO, on-policy self-distillation, regularization, or skipping, allowing training to adapt without external teachers, extra annotations, or additional sampling. Experiments on mathematical reasoning across Qwen3 and Qwen3.5 backbones show that Self-Routing consistently improves over uniform GRPO, uniform OPSD, fixed mixtures, and simpler routing baselines. Further analyses show that the routing distribution changes over training and reduces unnecessary updates on low-signal or already stable samples.</abstract>
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%0 Conference Proceedings
%T From Rollouts to Recipes: Self-Contained Post-Training for LLMs
%A Li, Yifei
%A Zhang, Lingling
%A Huang, Muye
%A Ma, Zihan
%A Liu, Jiashuai
%A Liu, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-rollouts
%X Post-training large language models usually apply a single training recipe to all samples, even though the model’s own rollouts reveal different sample-level learning states. We propose Self-Routing, a behavior-conditioned post-training framework that uses rollout correctness and confidence to decide how each sample should be optimized. Depending on its behavior state, a sample is routed to GRPO, on-policy self-distillation, regularization, or skipping, allowing training to adapt without external teachers, extra annotations, or additional sampling. Experiments on mathematical reasoning across Qwen3 and Qwen3.5 backbones show that Self-Routing consistently improves over uniform GRPO, uniform OPSD, fixed mixtures, and simpler routing baselines. Further analyses show that the routing distribution changes over training and reduces unnecessary updates on low-signal or already stable samples.
%U https://aclanthology.org/2026.emnlp-main.464/
%P 10640-10654
Markdown (Informal)
[From Rollouts to Recipes: Self-Contained Post-Training for LLMs](https://aclanthology.org/2026.emnlp-main.464/) (Li et al., EMNLP 2026)
ACL
- Yifei Li, Lingling Zhang, Muye Huang, Zihan Ma, Jiashuai Liu, and Jun Liu. 2026. From Rollouts to Recipes: Self-Contained Post-Training for LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10640–10654, Budapest, Hungary. Association for Computational Linguistics.