Learning Transferable Latent User Preferences for Human-Aligned Decision Making

arXiv:2605.12682 · cs.AI · Submitted 2026-05-12 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Learning Transferable Latent User Preferences for Human-Aligned Decision Making".

Jane: Here's the gist of the paper. The gist The CLIPR framework introduces an LLM-based system that learns actionable, transferable natural language rules representing latent user preferences from minimal conversational input to guide human-aligned decision making.

Tom: How it works CLIPR is a framework that learns preference information by interacting with the user and encoding it into "actionable, natural language rules that generalize across environments and tasks".

Jane: These rules are iteratively refined through adaptive feedback and applied to both in-distribution and out-of-distribution ambiguous tasks across multiple environments.

Tom: The learning process involves several key steps: 1.

Paper summary: Tom: So we’ve seen that CLIPR is this framework that learns actionable, transferable natural language rules from minimal conversational input to guide decision making. The core idea here is moving away from learning preferences through heavy feedback and instead extracting them as explicit, generalizable rules.

Jane: It sounds like the big claim is that these learned rules represent latent user preferences in a compact way, which lets the LLM use them at inference time to make decisions aligned with what a human actually wants. This addresses the problem of ambiguity in situations where there isn't an explicit instruction.

Lu: They are tackling the limitation that previous alignment methods, like RLHF or few-shot learning, often impose restrictive assumptions on preference structure and rely too much on indirect signals like rankings or demonstrations. CLIPR tries to use dynamic natural language interaction to infer intent more directly.

Meng: But how does it synthesize those rules from the conversation? Is it just summarizing what we talked about, or is there a deeper representation learning happening underneath that I need to worry about for deployment?

Lalam: Algorithm one shows that the LLM analyzes examples and dialogue history together to find underspecified preference dimensions <ref:2605.12682#pg1>. If those dimensions are insufficient, it generates a question for the user, collects the answer, and then synthesizes those rules from the combined dialogue and example set.

Tom: So it’s not just pattern matching; it’s an active elicitation process where the model actively seeks out what it doesn't know based on its current understanding of your goals. That sounds more like reasoning than just memorizing examples.

Jane: And they explicitly mention that this approach aims to extract actionable rules that are generalizable to a family of tasks, which is a big deal for practical application outside of one specific scenario.

Meng: If those rules are transferable, it means we could potentially deploy one set of preference rules across several different applications without needing extensive retraining for each one. That sounds like a significant win for engineering costs.

Lalam: The paper also points out that the method makes it difficult to capture complex or context-dependent preferences, although they note an exception called CIPHER which learns preferences through natural language feedback without requiring demonstrations, but that approach has high computational costs because it constantly needs feedback.

Tom: So CLIPR is trading off the constant need for feedback for the ability to generate rules that are reusable across different tasks. It's a trade-off between learning speed and generalization power.

Jane: And what they’ve shown empirically on datasets like AmbiK, Housekeep, and Mobile Manipulation is that it performs well in both in-distribution and out-of-distribution settings without needing to be retrained for those new environments.

Meng: That's the reality we need for production models. If it generalizes without retraining when faced with a slightly different task context, that reduces the maintenance burden considerably.

Lu: And their results on robustness are also telling; they showed that the learned rules are cross-model portable, meaning they aren't just artifacts of one specific LLM architecture, but they capture structures relevant to the preferences themselves.

Lalam: Plus, it showed it can recover from corrupted rules too, which is something you have to worry about when you're building a system that relies on learned internal states.

Tom: So the overall message is that we can get a more direct way for LLMs to understand user intent by learning explicit rules instead of just relying on implicit signals from interaction history.

Conclusion: Jane: So to wrap up, the paper "Learning Transferable Latent User Preferences for Human-Aligned Decision Making" by Hyk and Saisubramanian is suggesting that we can get LLMs to be better at handling ambiguous situations by learning explicit rules about user preferences.

Tom: The key implication is that instead of trying to fine-tune the model weights constantly through Reinforcement Learning from Human Feedback, we can extract a set of natural language rules and use those rules directly during inference for alignment.

Lu: This shifts the focus from making the model itself perfectly aligned internally to building a system that effectively extracts and manages these high-level preference instructions.

Meng: For someone running an AI service at scale, this means less reliance on massive amounts of continuous human interaction just to keep the preferences updated across your entire user base.

Jane: It’s about creating an internal, consistent model of user intent that is learned in a structured way and then applied directly to decision making without needing constant, expensive retraining cycles.

Lalam: The authors successfully demonstrated that this method can be cost-aware while still achieving high alignment accuracy on preference-ambiguous tasks.

Tom: It’s about being efficient, using minimal LLM calls compared to other learning methods, and getting a result that is portable across different tasks. That's the practical side of this research.

Alina Hyk, Sandhya Saisubramanian

Oregon State University

cs.AI

Submitted: 2026-05-12

Updated: 2026-10-19

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: The gist The CLIPR framework introduces an LLM-based system that learns actionable, transferable natural language rules representing latent user preferences from minimal conversational input to guide

Key concepts

Latent User Preferences
These are the hidden, unstated desires or guidelines users have for how ambiguous situations should be resolved. CLIPR aims to capture these preferences by extracting them from user conversations rather than relying on explicit instructions, allowing the agent to make decisions that align with the user's true intent.
Preference Rule Critic
This component monitors the agent's performance and decides when it is beneficial to update the learned preference rules. It uses specific metrics to determine if current rules are causing poor decision-making, triggering a refinement process only when necessary, thus saving computational effort.
Transferable Rules
The rules learned by CLIPR are designed to be general and applicable across different environments and tasks. This means the preference structures captured are semantic rather than tied to a single model or specific context, allowing the agent to maintain consistent decision-making even when faced with new, unseen situations.
Preference-Aligned Accuracy
This metric measures how well the agent's responses satisfy both explicit user requests and the inferred latent preference constraints. A high score indicates that the decisions made by the LLM are not only correct according to stated goals but also align with what is implicitly preferred by the user.

Terminology

Summary

The gist The CLIPR framework introduces an LLM-based system that learns actionable, transferable natural language rules representing latent user preferences from minimal conversational input to guide human-aligned decision making.

How it works

CLIPR is a framework that learns preference information by interacting with the user and encoding it into actionable, natural language rules that generalize across environments and tasks. These rules are iteratively refined through adaptive feedback and applied to both in-distribution and out-of-distribution ambiguous tasks across multiple environments.

The learning process involves several key steps:

  1. Example Set Initialization: CLIPR is initialized with a small example set S approximating the tasks the agent will assist the user with. Crucially, the candidate actions in S must include cases where multiple actions in C(x) are correct, but latent user preferences determine the preferred action a∗.

  2. Interactive Preference Elicitation: The LLM iteratively analyzes the examples in S alongside the current dialogue history D to identify which preference dimensions remain underspecified. This step grounds question generation in the example set rather than in the LLM’s priors over user preferences.

  3. Rule Synthesis: Once interaction is complete, the LLM uses the information in D and S to synthesize an explicit numbered list of preference rules R.

Adaptive Learning Mechanism

To overcome the limitation of static rules, CLIPR is extended with Adaptive CLIPR to support continuous refinement through dynamic feedback. This mechanism monitors agent performance using a preference rule critic that determines when updating the rules would be useful.

The adaptive feedback loop is formalized in Algorithm 2, which involves several sequential judgments:

  1. Intervention Gate: The critic intervenes if "µH − accB > α · σH or if H < 2".

  2. Rule Update Decision: The critic assesses whether a rule update is warranted based on user input, using the judgment WANT TO UPDATE RULES.

  3. Rule Update: If warranted and verified by a verification gate, the critic produces the complete numbered list of rules (including unchanged ones)

Empirical Evaluation

The framework was evaluated on three datasets: AmbiK, Housekeep, and Mobile Manipulation. In the user study with 30 participants, Adaptive CLIPR outperformed baselines in both in-distribution and out-of-distribution tasks.

The evaluation metrics used are:

  1. Preference-Aligned Accuracy: "measured as the proportion of responses that satisfy the conjunction of explicit constraints derived from the natural language user request query and latent preference constraints inferred from the user’s preference profile".

  2. Computational Efficiency score: Measured as E = A·N, where A denotes accuracy, C the cumulative number of LLM calls, and N the total number of action selections made by LLM.

Robustness and Transferability

The study demonstrated that CLIPR generalizes to out-of-distribution environments without retraining. Furthermore, the learned rules showed cross-model portability, indicating they capture task-relevant semantic preference structures rather than purely model-specific behaviors. The adaptive feedback mechanism also showed robustness when starting from poor initialization, recovering even under Contradictive Rules.

User Study Findings

Participants rated Adaptive CLIPR to have lower effort and frustration compared to baselines. The performance on OOD tasks was statistically better than CIPHER.

The paper concludes that CLIPR and Adaptive CLIPR offer improvement in alignment while remaining cost-aware. Future work should focus on Extending CLIPR to represent substantially more complex, conditional preferences. The framework successfully demonstrates that LLMs can be effective in maintaining a consistent internal model of user intent.

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D.4.1 Generate query. Builds a clarification question for the user based on the recent batch’s failures.

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D.4.3 Update rules. If the previous step returned YES, the critic produces the full revised rule set.

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D.

Improvements for AI systems

  1. Conversational Learning for Inferring Preferences (CLIPR) enables LLMs to learn actionable natural language rules from minimal interactions, allowing them to infer latent user preferences that guide decision making across different tasks and environments. This allows the AI system to select actions that align with implicit user goals, moving beyond explicit requirements.

  2. Adaptive CLIPR refines learned preference rules through an adaptive feedback mechanism where a preference rule critic monitors agent performance and decides when to seek additional information from the user based on failure rates, ensuring continuous improvement over time. This capability allows the system to improve alignment even when initial preference rules are absent or contradictory.

  3. The use of a rules critic in Adaptive CLIPR gates rule updates by comparing new rules against current performance, ensuring that rules are updated only if past performance does not degrade, which prevents the model from overfitting to test instances and maintains robustness. This mechanism ensures that learned preferences are stable and generalize well to out-of-distribution environments.

  4. The system can perform inference using learned rules by structuring the LLM response with a specific template, allowing it to Evaluate count scenarios while ensuring each output provides both an action number and a brief reasoning, enhancing explainability. This structured output reduces ambiguity in downstream decision making for complex queries.

Abstract

Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned decisions. Human-aligned decision-making requires accounting for both explicitly stated goals and latent user preferences that shape how ambiguous situations should be resolved. Existing approaches either learn these preferences through continual per-interaction feedback, which is costly for both the user and the agent, or keep them implicit in dialogue transcripts or example sets tied to a single model. We present CLIPR (Conversational Learning for Inferring Preferences and Reasoning), a framework that represents user's preferences as an explicit, compact set of natural-language rules used at inference time for human-aligned decision-making. CLIPR elicits these rules from conversational input, monitors decision accuracy, and consults the user when performance degrades unexpectedly. CLIPR revises the learned rules, based on the conversation, only if it does not reduce accuracy on scenarios already processed. Evaluations on three preference-ambiguous datasets show that CLIPR matches or outperforms baselines while using up to 96% fewer LLM calls than continual-feedback preference learning. Our results also show that rules learned by one LLM transfer unchanged to others, and that our approach can recover from corrupted rules. In a user study with 30 participants, CLIPR outperformed baselines and was rated as requiring less effort than continual-feedback learning.

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