In this work we focus on the problem of embodied task planning, which requires an agent to accomplish a complex task by separating it into atomic actions. Recent studies have developed LLM-based methods to address this problem. However, the executability and correctness of the generated plans remain suboptimal in complex environments and tasks. On the other hand, classical search algorithms have the potential to solve the planning problems perfectly, yet they require fully observable environments, known state transitions and goal state, which are often unattainable in general task domains. Therefore, we propose to implement an LLM-guided search algorithm that makes the search process possible and efficient by incorporating LLMs’ commonsense knowledge. Our method leverages LLMs to (1) translate natural language instructions into formatted goal conditions, (2) facilitate efficient exploration in partially observable environments, (3) implement a rule-based world simulator, and (4) prune the space of candidate actions to improve search efficiency. Experimental results on VirtualHome and ALFWorld demonstrate the superior performance of our agent.

Aligning large language models (LLMs) with human preferences is challenging due to the inherent heterogeneity and multi-dimensionality of these preferences. Existing inference-time multi-objective alignment methods often rely on fixed weights to combine preference models. However, different tokens contribute unequally to preference objectives during decoding. Ignoring such token-level variation may cause the loss of preference information and lead to objective interference. To alleviate this, we utilize the implicit rewards of preference models to estimate importance differences between tokens, and propose a new training-free and reward-free framework TAW to dynamically balance multiple objectives for inference-time alignment. We design the relative entropy and marginal entropy estimators to quantify the informativeness and uncertainty of preference objectives. TAW with the estimator ensures that more informative or reliable preferences are emphasized, while weak or uncertain signals are suppressed. Experiments on two LLM alignment benchmarks show that TAW outperforms strong baselines without predefined weightings, while also achieves an overall optimal Pareto front when explicit preference weighting is applied.