SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environments

• URL: http://arxiv.org/abs/2512.09897v1
• Date: Wed, 10 Dec 2025 18:26:14 GMT
• Title: SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environments
• Authors: Haoye Lu, Pavan Seshadri, Kaheer Suleman,
• Abstract summary: Long-term planning in text-based environments presents significant challenges due to open-ended action spaces, ambiguous observations, and sparse feedback.<n>Recent research suggests that large language models (LLMs) encode rich semantic knowledge about the world, which can be valuable for guiding agents in high-level reasoning and planning across both embodied and purely textual settings.<n>Existing approaches often depend heavily on querying LLMs during training and inference, making them computationally expensive and difficult to deploy efficiently.<n>We introduce SCOPE (Subgoal-COnditioned Pretraining for Efficient planning), a one-shot hierarchical planner that leverages LLM-generated subgoal
• Score: 4.375012768093524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-term planning in complex, text-based environments presents significant challenges due to open-ended action spaces, ambiguous observations, and sparse feedback. Recent research suggests that large language models (LLMs) encode rich semantic knowledge about the world, which can be valuable for guiding agents in high-level reasoning and planning across both embodied and purely textual settings. However, existing approaches often depend heavily on querying LLMs during training and inference, making them computationally expensive and difficult to deploy efficiently. In addition, these methods typically employ a pretrained, unaltered LLM whose parameters remain fixed throughout training, providing no opportunity for adaptation to the target task. To address these limitations, we introduce SCOPE (Subgoal-COnditioned Pretraining for Efficient planning), a one-shot hierarchical planner that leverages LLM-generated subgoals only at initialization to pretrain a lightweight student model. Unlike prior approaches that distill LLM knowledge by repeatedly prompting the model to adaptively generate subgoals during training, our method derives subgoals directly from example trajectories. This design removes the need for repeated LLM queries, significantly improving efficiency, though at the cost of reduced explainability and potentially suboptimal subgoals. Despite their suboptimality, our results on the TextCraft environment show that LLM-generated subgoals can still serve as a strong starting point for hierarchical goal decomposition in text-based planning tasks. Compared to the LLM-based hierarchical agent ADaPT (Prasad et al., 2024), which achieves a 0.52 success rate, our method reaches 0.56 and reduces inference time from 164.4 seconds to just 3.0 seconds.

Related papers

• rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection [49.74493901036598]
  Large language models (LLMs) are post-trained through reinforcement learning (RL) to evolve into Reasoning Language Models (RLMs)<n>This paper proposes a novel reinforced strategy injection mechanism (rSIM) that enables any LLM to become an RLM by employing a small planner.<n> Experimental results show that rSIM enables Qwen2.5-0.5B to become an RLM and significantly outperform Qwen2.5-14B.
  arXiv  Detail & Related papers  (2025-12-09T06:55:39Z)
• Learning to Reason and Navigate: Parameter Efficient Action Planning with Large Language Models [63.765846080050906]
  This paper proposes a novel parameter-efficient action planner using large language models (PEAP-LLM) to generate a single-step instruction at each location.<n>Experiments show the superiority of our proposed model on REVERIE compared to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2025-05-12T12:38:20Z)
• Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning [16.654435148168172]
  Large Language Models (LLMs) have shown remarkable promise in reasoning and decision-making.<n>We propose an LLM-guided hierarchical RL framework, termed LDSC, to enhance sample efficiency, generalization, and multi-task adaptability.
  arXiv  Detail & Related papers  (2025-03-24T15:49:56Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities.<n>LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands.<n>We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• Sub-goal Distillation: A Method to Improve Small Language Agents [21.815417165548187]
  Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks. We propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7%.
  arXiv  Detail & Related papers  (2024-05-04T20:34:06Z)
• Empowering Large Language Models on Robotic Manipulation with Affordance Prompting [23.318449345424725]
  Large language models fail to interact with the physical world by generating control sequences properly. Existing LLM-based approaches circumvent this problem by relying on additional pre-defined skills or pre-trained sub-policies. We propose a framework called LLM+A(ffordance) where the LLM serves as both the sub-task planner and the motion controller.
  arXiv  Detail & Related papers  (2024-04-17T03:06:32Z)
• LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents [10.936460061405157]
  We propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs. Our approach does not assume access to a propreitary or a fine-tuned LLM, nor does it require pre-trained policies that achieve the sub-goals proposed by the LLM.
  arXiv  Detail & Related papers  (2023-10-14T00:07:03Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.