Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing methods typically train a separate adapter for each user or learn a reward model whose scores depend on the user. Despite explicitly optimizing for each user, these methods must learn from limited observations and therefore suffer from data sparsity and poor generalization to unseen users and domains. In-context learning (ICL) and Context Steering (CoS) can instead provide more effective personalization by conditioning the base LM directly on user context and leveraging its pretrained capabilities without per-user training. Yet neither adapts the influence of that context across decoding steps: ICL leaves it uncontrolled, whereas CoS applies a fixed steering coefficient and requires two LM forward passes per step. We propose Cautious Context Steering (CCS), which adds a lightweight adapter to a frozen backbone LM to decide at each token whether and how strongly user context should affect generation. The adapter learns this behavior from an oracle context-conditioned LM and preserves the base LM when the context is not helpful. A single CCS adapter trained on only one dataset improves generation quality both in-domain and across four out-of-distribution personalization benchmarks, demonstrating robust generalization to new users and domains. CCS also avoids per-user fine-tuning and the additional context-conditioned forward pass required by CoS, substantially reducing inference cost.
Gihoon Kim, Jeyoung Lee, Suhan Woo et al.· 0 citations
Large Language Models (LLMs) have demonstrated strong generalization and reasoning capabilities across a wide range of domains, including embodied decision making and robotics. Despite this progress, existing reinforcement-based embodied agents often struggle with novel tasks requiring complex spatial reasoning and multi-object manipulation when relying solely on the robot’s egocentric view. In this paper, we propose SPaRL (Spatially-aware Reinforcement Learning with Language), a novel framework that explicitly integrates a structured 3D scene graph into an LLM-based reinforcement learning policy. By representing objects and their spatial relationships in a compact, semantically meaningful graph and conditioning it on task instructions, SPaRL provides the policy with explicit relational context beyond raw visual inputs. The scene graph is dynamically pruned to retain instruction-relevant objects and relations, serialized into natural language, and jointly processed with visual observations and task descriptions by a frozen LLM backbone. We evaluate SPaRL on language-conditioned rearrangement tasks in Habitat. Results show that incorporating an instruction-conditioned scene graph consistently improves performance over a vision-only LLM policy. Across curriculum training, SPaRL achieved improved performance on the Language Rearrangement benchmark. These results suggest that explicit spatial representations provide useful inductive bias for object reasoning, particularly as task difficulty increases.
Jeyoung Lee, Jaewon Lee, J. Oh et al.· 2026 23rd International Conf...· 0 citations
A lightweight and projection-free autoencoder that directly outputs compact, Contrastive Language–Image Pre-training (CLIP)-scale latent vectors trained toward the CLIP embedding space, achieving superior signal reconstruction fidelity across time and frequency domains using significantly fewer parameters than traditional masked autoencoder baselines.
Jeyoung Lee, Jaekwan Ahn, Jaeseung Sim et al.· Italian National Conference...· 0 citations