@inproceedings{volovikova-etal-2026-self,
title = "Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning",
author = "Volovikova, Zoya and
Sorokin, Nikita and
Lukashevskiy, Dmitriy and
Panov, Aleksandr and
Skrynnik, Alexey",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1691/",
pages = "33863--33882",
ISBN = "979-8-89176-395-1",
abstract = "We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language model to generate and refine high-level plans through a self-learning mechanism, reducing the need for manual dataset annotation. Our approach involves iterative co-training: an RL agent is trained to follow the generated plans, while the language model adapts and modifies these plans based on RL feedback and preferences. This creates a feedback loop where both the agent and the planner improve jointly. We validate our framework in environments with rich dynamics and stochasticity. Results show that SuperIgor agents adhere to instructions more strictly than baseline methods, while also demonstrating strong generalization to previously unseen instructions."
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%0 Conference Proceedings
%T Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
%A Volovikova, Zoya
%A Sorokin, Nikita
%A Lukashevskiy, Dmitriy
%A Panov, Aleksandr
%A Skrynnik, Alexey
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F volovikova-etal-2026-self
%X We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language model to generate and refine high-level plans through a self-learning mechanism, reducing the need for manual dataset annotation. Our approach involves iterative co-training: an RL agent is trained to follow the generated plans, while the language model adapts and modifies these plans based on RL feedback and preferences. This creates a feedback loop where both the agent and the planner improve jointly. We validate our framework in environments with rich dynamics and stochasticity. Results show that SuperIgor agents adhere to instructions more strictly than baseline methods, while also demonstrating strong generalization to previously unseen instructions.
%U https://aclanthology.org/2026.findings-acl.1691/
%P 33863-33882
Markdown (Informal)
[Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning](https://aclanthology.org/2026.findings-acl.1691/) (Volovikova et al., Findings 2026)
ACL