RPS: Information Elicitation with Reinforcement Prompt Selection

Enmao Diao; Haonan Huang; Jingyao Lu; Su Yao; Tao Wang; Xibo Wang; Xingyan Chen; Zhiqiang Hu

arxiv: 2604.13817 · v1 · submitted 2026-04-15 · 💻 cs.LG

RPS: Information Elicitation with Reinforcement Prompt Selection

Tao Wang , Jingyao Lu , Xibo Wang , Haonan Huang , Su Yao , Zhiqiang Hu , Xingyan Chen , Enmao Diao This is my paper

Pith reviewed 2026-05-10 14:01 UTC · model grok-4.3

classification 💻 cs.LG

keywords information elicitationreinforcement learningprompt selectiondialogue systemsIELegal benchmarklarge language modelsadaptive querying

0 comments

The pith

Reinforcement Prompt Selection uses reinforcement learning to choose prompts adaptively and elicit concealed user information better than static methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper frames information elicitation as the challenge of getting users to reveal known but withheld details during open-ended LLM conversations, a problem that arises in personal assistants, tutoring, legal, and clinical settings. It proposes RPS, a lightweight RL approach that treats prompt choice as a sequential decision process to maximize the information obtained over multiple turns. A synthetic experiment first shows that an RL agent can beat a random-query baseline. RPS is then evaluated on IELegal, a new benchmark derived from real legal case documents that simulates the task of uncovering case-relevant facts through dialogue. In that setting the learned policy beats several static prompt baselines.

Core claim

RPS learns a policy over a pool of prompts to adaptively elicit concealed or incompletely expressed information from users through dialogue. In a controlled synthetic experiment the reinforcement-learning agent outperforms a random query baseline. On the IELegal benchmark constructed from real legal case documents, RPS exceeds the performance of static prompt baselines, showing that adaptive prompt selection improves the ability of LLM-driven dialogue systems to gather critical information.

What carries the argument

Reinforcement Prompt Selection (RPS), a lightweight reinforcement learning framework that formulates prompt selection as a sequential decision-making problem whose actions are drawn from a pool of prompts and whose objective is to maximize cumulative information elicited across dialogue turns.

If this is right

A learned policy can outperform both random and fixed prompt strategies for gathering user information in dialogue.
The IELegal benchmark supplies a reproducible testbed for comparing elicitation methods in a legal-fact-finding context.
LLM systems in domains that require complete user input may reach higher accuracy by replacing static prompts with an adaptive selection policy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same policy-learning approach could be applied to medical or tutoring dialogues where patients or students similarly withhold details.
Integrating RPS with explicit information-gain estimators or belief tracking could further reduce the number of turns needed to reach a complete picture.

Load-bearing premise

Performance gains observed on the synthetic experiment and the constructed IELegal dataset will transfer to real open-ended user interactions where information withholding stems from privacy, ambiguity, or social factors.

What would settle it

Running RPS and static prompt baselines in live conversations with human participants who are instructed to withhold specific facts for privacy or hesitation reasons, then measuring whether the learned policy still elicits more complete information.

Figures

Figures reproduced from arXiv: 2604.13817 by Enmao Diao, Haonan Huang, Jingyao Lu, Su Yao, Tao Wang, Xibo Wang, Xingyan Chen, Zhiqiang Hu.

**Figure 1.** Figure 1: RPS uses reinforcement learning to adaptively select policy prompts that elicit users’ concealed information I − through multi-turn dialogue. At each turn t, the model updates its information estimate Iˆt−1, computes a reward rt based on information gain, and trains a policy πθ to optimize prompt selection from a predefined pool. We quantify performance using the average of componentwise KL divergences be… view at source ↗

**Figure 2.** Figure 2: GMM-based simulation of adaptive information elicitation under unbiased and biased user disclosure. (a) and (c) report the KL divergence between the estimated and true information distributions over dialogue rounds for the unbiased and biased user settings. (b) and (d) visualize the corresponding GMM fits. Across both settings, the RL–based querying strategy consistently achieves lower KL divergence than t… view at source ↗

**Figure 3.** Figure 3: Comparison of RPS (blue) against baselines on the (a) IELegal-base and (b) IELegal-augment datasets. The y-axis indicates the semantic similarity between the cumulative extracted information and the ground truth information in different rounds t. RPS consistently outperforms all baselines by the end of the dialogue. prompts users to elaborate on omitted details by drawing attention to gaps in their narrat… view at source ↗

**Figure 4.** Figure 4: Comparison of RPS (blue), RLPrompt and the GRIPS variant based on four fixed prompt strategies on the IELegal-base dataset. The performance of both GRIPS and RLPrompt remains consistently inferior to that of RPS, and in several cases even falls below the results obtained using the original domain-specific prompts alone. of the dialogue, confirming its advantage in adaptively selecting effective prompt str… view at source ↗

read the original abstract

Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in open-ended conversations remains limited. In many interactive AI applications, such as personal assistants, tutoring systems, and legal or clinical support, users often withhold sensitive or uncertain information due to privacy concerns, ambiguity, or social hesitation. This makes it challenging for LLMs to gather complete and contextually relevant inputs. In this work, we define the problem of information elicitation in open-ended dialogue settings and propose Reinforcement Prompt Selection (RPS), a lightweight reinforcement learning framework that formulates prompt selection as a sequential decision-making problem. To analyze this problem in a controlled setting, we design a synthetic experiment, where a reinforcement learning agent outperforms a random query baseline, illustrating the potential of policy-based approaches for adaptive information elicitation. Building on this insight, RPS learns a policy over a pool of prompts to adaptively elicit concealed or incompletely expressed information from users through dialogue. We also introduce IELegal, a new benchmark dataset constructed from real legal case documents, which simulates dialogue-based information elicitation tasks aimed at uncovering case-relevant facts. In this setting, RPS outperforms static prompt baselines, demonstrating the effectiveness of adaptive prompt selection for eliciting critical information in LLM-driven dialogue systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RPS treats prompt selection as an RL problem to pull out concealed info in dialogues and adds the IELegal benchmark, but the gains rest on artificial simulations that may not match real withholding behavior.

read the letter

The paper's main move is to cast prompt choice as a sequential RL task so an LLM can adaptively draw out information users are holding back in open conversation. They first show an RL agent beating a random baseline in a synthetic setup, then introduce IELegal, a dataset built from legal case documents that turns fact-finding into a dialogue elicitation problem, and report that their RPS policy beats static prompt baselines there. The new benchmark and the clean framing of adaptive elicitation are the concrete additions; prior RL-for-prompting work exists, but applying it specifically to concealed-information recovery in this way looks fresh on the abstract alone. The empirical comparison is straightforward and gives a clear signal that static prompts are not optimal for this task. The soft spots are the lack of any detail on state representation, reward design, training stability, or statistical significance, which leaves the reported gains hard to assess or reproduce. The bigger issue is the benchmark itself: IELegal simulates withholding through scripted or document-derived mechanisms rather than modeling privacy costs, ambiguity, or social factors, so the RL policy may simply be fitting to benchmark artifacts. The stress-test note is on target here, and that undercuts how much the results tell us about real user interactions. This is aimed at researchers working on interactive LLM systems for legal, clinical, or privacy-sensitive domains. A reader looking for new benchmarks or RL prompting ideas could extract some value from the comparisons, but the work is still at the proof-of-concept stage. It deserves peer review so referees can press on the RL implementation details and the benchmark's ecological validity.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Reinforcement Prompt Selection (RPS), a lightweight RL framework that formulates prompt selection as a sequential decision-making problem to adaptively elicit concealed or incompletely expressed information from users in open-ended LLM dialogues. It reports that an RL agent outperforms a random query baseline in a synthetic experiment and that RPS outperforms static prompt baselines on the newly introduced IELegal benchmark, which is constructed from real legal case documents to simulate dialogue-based fact uncovering.

Significance. If the empirical gains are shown to be robust and not artifacts of the specific setups, RPS could provide a practical method for improving information elicitation in LLM-driven applications such as legal support or clinical assistants. The introduction of the IELegal benchmark is a constructive addition for evaluating elicitation techniques. The work is empirical and compares against simple baselines, which is a reasonable starting point but requires more methodological transparency to achieve broader impact.

major comments (2)

[Abstract] Abstract: The abstract reports that an RL agent beats random and static baselines in synthetic and IELegal settings, but provides no details on reward design, state representation, training stability, statistical significance, or controls for prompt-pool size, making it impossible to verify whether the central performance gains stem from adaptive elicitation.
[IELegal Benchmark] IELegal benchmark construction: The benchmark simulates uncovering case facts via dialogue, but the concealment mechanism (scripted/random fact-hiding versus modeled privacy costs, ambiguity, or social dynamics) is unspecified. This is load-bearing because non-adversarial or deterministic responses would allow the policy to optimize for benchmark artifacts rather than genuine adaptive elicitation, undermining transfer claims.

minor comments (2)

[Methods] Methods: Provide pseudocode or a clear description of the RPS policy network, action space, and RL algorithm (e.g., PPO or REINFORCE) to support reproducibility.
[Experiments] Experiments: Report the number of independent runs, standard deviations, and any statistical tests for the reported outperformance metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and indicate the revisions we will make to improve transparency and detail.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract reports that an RL agent beats random and static baselines in synthetic and IELegal settings, but provides no details on reward design, state representation, training stability, statistical significance, or controls for prompt-pool size, making it impossible to verify whether the central performance gains stem from adaptive elicitation.

Authors: We agree that the abstract is high-level and omits methodological specifics. These elements are described in the body of the paper (reward design and state representation in Section 3, training and stability in Section 4, statistical significance and prompt-pool controls in Section 5). To address the concern, we will revise the abstract to include a concise reference to the RL formulation and key experimental controls, allowing readers to better assess the source of the reported gains. revision: yes
Referee: [IELegal Benchmark] IELegal benchmark construction: The benchmark simulates uncovering case facts via dialogue, but the concealment mechanism (scripted/random fact-hiding versus modeled privacy costs, ambiguity, or social dynamics) is unspecified. This is load-bearing because non-adversarial or deterministic responses would allow the policy to optimize for benchmark artifacts rather than genuine adaptive elicitation, undermining transfer claims.

Authors: We acknowledge that the concealment mechanism is central to interpreting the benchmark results and transferability. We will expand the benchmark construction section to explicitly describe the fact-hiding procedure used in IELegal and add a limitations paragraph discussing the assumptions (e.g., scripted vs. dynamic user behavior) and their implications for real-world applicability. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation stands independent of inputs.

full rationale

The paper defines an information elicitation problem, introduces an RL policy for prompt selection (RPS), and reports outperformance on a synthetic experiment plus the constructed IELegal benchmark against random and static baselines. No equations are presented that reduce reported gains to quantities defined by fitted parameters within the paper. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing justifications. The central results are experimental comparisons, which remain falsifiable against external benchmarks and do not collapse by construction to the method's own definitions or training data.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard RL assumptions that prompt selection can be modeled as a Markov decision process with observable user responses as states and information gain as reward; no new entities are postulated.

free parameters (1)

RL policy hyperparameters
Standard RL training parameters such as learning rate and discount factor are required but not specified in the abstract.

axioms (1)

domain assumption User responses provide sufficient signal to update the prompt-selection policy
Invoked when the method treats dialogue turns as sequential decisions whose outcomes improve future prompt choice.

pith-pipeline@v0.9.0 · 5548 in / 1225 out tokens · 24366 ms · 2026-05-10T14:01:24.233165+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · 1 internal anchor

[1]

Adam: A Method for Stochastic Optimization

ISBN 9787511874702. [in Chinese]. Han, D. and Peng, Y .Fal¨u zixun [Legal Consultation]. Law Press China, 2024. ISBN 9787519791773. [in Chinese]. Kingma, D. P. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Kostric, I., Balog, K., and Radlinski, F. Generating usage- related questions for preference elicitation in convers...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

The foundation of GRIPS is the editing and searching of the policy prompt

Policy Decomposition and Atomic Operations. The foundation of GRIPS is the editing and searching of the policy prompt. • Policy Decomposition:We treat the lawyer’s Policy Prompt as a sequence of semantic units. segment the policy text into Sentence-level Phrases based on punctuation marks (Chinese periods, question marks, exclamation points, and newlines)...

work page
[3]

•Initialization:The dialog begins with the initial policy prompt corresponding to the fixed strategy

Experimental Procedure. •Initialization:The dialog begins with the initial policy prompt corresponding to the fixed strategy. • Context-Aware Editing:The GRIPS performs gradient-free edit operations (such as deletion, rephrasing, or swapping of semantic units) conditioned on the current dialog history to generate a set of candidate policy prompts. • Evalu...

work page
[4]

Context-Aware Editing Prompt

Prompt. Context-Aware Editing Prompt. strategy Scenario:Simulating an initial consultation between a lawyer and a client to gather factual information about a legal case. Role:You are a strategy expert, specialized in refining and optimizing questioning strategy. Task:Based on the existing dialogue history and the strategy used in the previous turn, you a...

work page 2026
[5]

Problem Formalization. The discrete prompt optimization is formalized as a Markov Decision Process (MDP): • Objective.The goal is to train a Policy Network πθ to maximize the expected reward R, ensuring the generated fixed-length discrete prompt sequencez=maximizes the performance on open-ended dialogue. • Environment.The dialogue logic in the legal field...

work page
[6]

Policy Network. • Frozen Policy LM.The Policy Network is constructed upon a frozen, compact pre-trained LM (e.g., distilgpt2), which functions to encode the state s and the partial prompt z<t into high-quality contextual embeddings. We use Qwen2.5-0.5B (Yang et al., 2024) as the Policy Network. • Trainable MLP.The component containing all trainable parame...

work page 2024
[7]

During the training phase, the policy network must actively explore the discrete prompt space and generate the necessary data for gradient calculation

Sequence Exploration and Log Likelihood Tracking. During the training phase, the policy network must actively explore the discrete prompt space and generate the necessary data for gradient calculation. • Exploration (Top-K Sampling).The policy network uses Top-K sampling (e.g., K= 256 ) to probabilistically select the next tokenz t. 13 Submission and Form...

work page 2026
[8]

• Environment Interaction.Generate a batch of candidate lawyer prompts ( Z(s)) for the same conversation state s, and sequentially conduct multiple rounds of dialogue

Environment Interaction, Reward Calculation and Stabilization. • Environment Interaction.Generate a batch of candidate lawyer prompts ( Z(s)) for the same conversation state s, and sequentially conduct multiple rounds of dialogue. •Reward Calculation.Compute a scalar, piecewise rewardR, which is a composite metric. • Reward Stabilization.Apply input-speci...

work page
[9]

Policy Update. We implement the policy gradient objective, minimized using the formula: Loss=−E[A·logπ θ(z|s)] The Normalized Rewards (from 4) function as the Advantage A, and the optimization updates the MLP parameters derived from the accumulatedlogπ θ(z|s). C. Agent This section presents the prompts used for both the user and the model, including the s...

work page
[10]

If a question directly and explicitly pertains to adverse information, provide an honest and straightforward answer

work page
[11]

14 Submission and Formatting Instructions for ICML 2026

If a question does not directly or precisely target adverse information, the response may omit or partially conceal such information. 14 Submission and Formatting Instructions for ICML 2026

work page 2026
[12]

All questions regarding favorable or neutral information must be answered truthfully and completely

work page
[13]

I don’t know

For questions concerning aspects not mentioned in the case materials, respond with "I don’t know." Limitation: Each response must be no longer than 30 Chinese characters (or equivalently concise in another language), to simulate realistic and focused user replies. Model Prompt.The model prompt specifies that, within the context of a simulated initial cons...

work page
[14]

Maintain strict adherence to the specified communication strategies throughout the interaction

work page
[15]

After receiving each client response, analyze the reply carefully and formulate a follow-up question according to the relevant strategy

work page
[16]

Each question should be concise, with a maximum length of 40 Chinese characters (or equivalent in other languages)

work page
[17]

Could you describe in detail what happened on that day?

Proceed with one question at a time to ensure clarity and focus. 15 Submission and Formatting Instructions for ICML 2026 C.2. Policy Strategy 1: Exploratory Information Gathering.This strategy encourages open-ended dialogue to elicit broad and spontaneous user responses. By using prompts such as“Could you describe in detail what happened on that day?”, th...

work page 2026

[1] [1]

Adam: A Method for Stochastic Optimization

ISBN 9787511874702. [in Chinese]. Han, D. and Peng, Y .Fal¨u zixun [Legal Consultation]. Law Press China, 2024. ISBN 9787519791773. [in Chinese]. Kingma, D. P. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Kostric, I., Balog, K., and Radlinski, F. Generating usage- related questions for preference elicitation in convers...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[2] [2]

The foundation of GRIPS is the editing and searching of the policy prompt

Policy Decomposition and Atomic Operations. The foundation of GRIPS is the editing and searching of the policy prompt. • Policy Decomposition:We treat the lawyer’s Policy Prompt as a sequence of semantic units. segment the policy text into Sentence-level Phrases based on punctuation marks (Chinese periods, question marks, exclamation points, and newlines)...

work page

[3] [3]

•Initialization:The dialog begins with the initial policy prompt corresponding to the fixed strategy

Experimental Procedure. •Initialization:The dialog begins with the initial policy prompt corresponding to the fixed strategy. • Context-Aware Editing:The GRIPS performs gradient-free edit operations (such as deletion, rephrasing, or swapping of semantic units) conditioned on the current dialog history to generate a set of candidate policy prompts. • Evalu...

work page

[4] [4]

Context-Aware Editing Prompt

Prompt. Context-Aware Editing Prompt. strategy Scenario:Simulating an initial consultation between a lawyer and a client to gather factual information about a legal case. Role:You are a strategy expert, specialized in refining and optimizing questioning strategy. Task:Based on the existing dialogue history and the strategy used in the previous turn, you a...

work page 2026

[5] [5]

Problem Formalization. The discrete prompt optimization is formalized as a Markov Decision Process (MDP): • Objective.The goal is to train a Policy Network πθ to maximize the expected reward R, ensuring the generated fixed-length discrete prompt sequencez=maximizes the performance on open-ended dialogue. • Environment.The dialogue logic in the legal field...

work page

[6] [6]

Policy Network. • Frozen Policy LM.The Policy Network is constructed upon a frozen, compact pre-trained LM (e.g., distilgpt2), which functions to encode the state s and the partial prompt z<t into high-quality contextual embeddings. We use Qwen2.5-0.5B (Yang et al., 2024) as the Policy Network. • Trainable MLP.The component containing all trainable parame...

work page 2024

[7] [7]

During the training phase, the policy network must actively explore the discrete prompt space and generate the necessary data for gradient calculation

Sequence Exploration and Log Likelihood Tracking. During the training phase, the policy network must actively explore the discrete prompt space and generate the necessary data for gradient calculation. • Exploration (Top-K Sampling).The policy network uses Top-K sampling (e.g., K= 256 ) to probabilistically select the next tokenz t. 13 Submission and Form...

work page 2026

[8] [8]

• Environment Interaction.Generate a batch of candidate lawyer prompts ( Z(s)) for the same conversation state s, and sequentially conduct multiple rounds of dialogue

Environment Interaction, Reward Calculation and Stabilization. • Environment Interaction.Generate a batch of candidate lawyer prompts ( Z(s)) for the same conversation state s, and sequentially conduct multiple rounds of dialogue. •Reward Calculation.Compute a scalar, piecewise rewardR, which is a composite metric. • Reward Stabilization.Apply input-speci...

work page

[9] [9]

Policy Update. We implement the policy gradient objective, minimized using the formula: Loss=−E[A·logπ θ(z|s)] The Normalized Rewards (from 4) function as the Advantage A, and the optimization updates the MLP parameters derived from the accumulatedlogπ θ(z|s). C. Agent This section presents the prompts used for both the user and the model, including the s...

work page

[10] [10]

If a question directly and explicitly pertains to adverse information, provide an honest and straightforward answer

work page

[11] [11]

14 Submission and Formatting Instructions for ICML 2026

If a question does not directly or precisely target adverse information, the response may omit or partially conceal such information. 14 Submission and Formatting Instructions for ICML 2026

work page 2026

[12] [12]

All questions regarding favorable or neutral information must be answered truthfully and completely

work page

[13] [13]

I don’t know

For questions concerning aspects not mentioned in the case materials, respond with "I don’t know." Limitation: Each response must be no longer than 30 Chinese characters (or equivalently concise in another language), to simulate realistic and focused user replies. Model Prompt.The model prompt specifies that, within the context of a simulated initial cons...

work page

[14] [14]

Maintain strict adherence to the specified communication strategies throughout the interaction

work page

[15] [15]

After receiving each client response, analyze the reply carefully and formulate a follow-up question according to the relevant strategy

work page

[16] [16]

Each question should be concise, with a maximum length of 40 Chinese characters (or equivalent in other languages)

work page

[17] [17]

Could you describe in detail what happened on that day?

Proceed with one question at a time to ensure clarity and focus. 15 Submission and Formatting Instructions for ICML 2026 C.2. Policy Strategy 1: Exploratory Information Gathering.This strategy encourages open-ended dialogue to elicit broad and spontaneous user responses. By using prompts such as“Could you describe in detail what happened on that day?”, th...

work page 2026