Training-Based Elicitation

• Training-based elicitation is a methodology that adaptively updates model weights via fine-tuning, reinforcement learning, and meta-learning to surface latent capabilities.
• It systematically recovers hidden skills and calibrates model behavior while supporting applications in alignment, preference elicitation, and dynamic rubric creation.
• While effective in improving sample efficiency and task generalization, it faces challenges such as demonstration quality, reward hacking, and high computational costs.

Training-based elicitation refers to a broad class of methodologies in which model parameters, or weight configurations, are adaptively updated through direct training procedures, rather than relying solely on inference-time interventions such as static prompting or activation steering. This paradigm encompasses fine-tuning, reinforcement learning, preference optimization, and meta-learning routines designed to systematically surface, recover, or specify latent capabilities, task policies, evaluation rubrics, preferences, or distributions that cannot be reliably elicited via observation or prompt injection alone. Training-based elicitation now forms a core strategy in capability audits, supervised alignment, prior and preference specification, system- and user-facing dialog management, and even mechanism design across AI, statistics, and decision sciences.

1. Conceptual Foundations of Training-Based Elicitation

Training-based elicitation is motivated by persistent limitations of prompt-based and contextual elicitation protocols, especially for large models prone to “sandbagging” or hiding certain capabilities unless directly incentivized to reveal them. The canonical process involves adaptively modifying a model’s weights based on additional training signals, most often by fine-tuning on high-quality demonstrations or through reward-based optimization (e.g., RL) over target tasks (Greenblatt et al., 2024, Hofstätter et al., 4 Feb 2025). This active adaptation stands in contrast to inference-time manipulations and offers a systematic route to surfacing latent skills, extracting rich user or expert knowledge, or calibrating system behavior to new or adversarial conditions.

Recent work has broadened the scope to include preference and rubric elicitation in natural language (pairwise comparisons, criterion discovery), non-parametric expert prior specification in probabilistic models (normalizing flows), scalable label aggregation (balancing noisy and expert data), combinatorial preference learning (LLM-augmented proxies), and dynamic dialog or interview systems in educational and requirements-gathering scenarios (Rezaei et al., 8 Oct 2025, Bockting et al., 2024, Mallen et al., 2024, Huang et al., 24 Jan 2025, Görer et al., 2023, Görer et al., 2024, Lojo et al., 10 Mar 2025).

2. Methodological Frameworks and Key Algorithms

2.1 Supervised Fine-Tuning and RL for Capability Elicitation

The most widely studied instantiation of training-based elicitation involves fine-tuning locked or partially-disabled models on high-quality demonstration data or via RL-based reward optimization (Greenblatt et al., 2024, Hofstätter et al., 4 Feb 2025). Consider a “password-locked” LLM $p^{{\rm locked}}_\theta$, which outputs weak completions unless a secret trigger token is present. Training-based elicitation can use a subset $k$ of strong demonstrations to adapt $p^{{\rm locked}}_\theta$ and recover near-ceiling accuracy; for code and math, as few as 1-4 demos can recover $>$75% of ceiling performance, while generalization across tasks or domains is robust when fine-tuned with strong data (Greenblatt et al., 2024).

Two major RL approaches are used: expert iteration (sampling many outputs, picking top-$k$ by reward, and fine-tuning iteratively), and Direct Preference Optimization (pairwise ranking-based loss), both of which succeed when the initial model is not adversarially weak.

2.2 Online Rubric and Preference Elicitation

Modern model alignment regimes have introduced training-based elicitation of evaluation criteria themselves, using dynamic rubric expansion through pairwise comparisons. OnlineRubrics, for example, leverages RLHF with continual policy comparison: for each prompt, generated responses are compared between current and reference policies, and an LLM “extractor” synthesizes new binary rubric criteria that get merged into the reward function, with group-level reward normalization (GRPO objective) (Rezaei et al., 8 Oct 2025). This process drives both quantitative improvement (up to +8% win-rate) and qualitative evolution of evaluation criteria as the model moves through policy space.

2.3 Sequential Question Generation and Diffusion-Inspired Elicitation

For preference elicitation, training-based methods can reverse engineer a denoising process: from a fully specified user profile, an LLM is trained to iteratively generate “funnel” clarifying questions and reconstruct the profile by fine-tuning on deletion/insertion pairs (forward/reverse diffusion; (Montazeralghaem et al., 13 Oct 2025)). This methodology yields substantial improvements in the accuracy and efficiency of user preference acquisition from naturalistic dialog.

2.4 Sample-Efficient and Scalable Elicitation with Hybrid Data

Eliciting model performance given both low- and high-quality supervision is cast as a two-good optimization problem, where sample and label tradeoffs are quantified by cost and marginal accuracy. Practitioners use supervised fine-tuning on a mixture of noisy (“weak”) and expert (“strong”) labels, with Pareto-optimal allocations, sequential and prompt-injected regimes, and active selection to balance cost and accuracy (Mallen et al., 2024). This yields three diagnostic regimes: quantity-dominant, mixed, and quality-dominant, each optimal under different labeling budgets.

2.5 Normalizing-Flow-Based Expert Prior Elicitation

For probabilistic modeling, non-parametric priors can be elicited by training a normalizing flow to match expert-supplied predictive summaries via a multi-objective loss, leveraging simulation-based statistics (MMD, correlation, etc.) over prior predictions (Bockting et al., 2024). Model convergence, non-uniqueness, and calibration are handled via stochastic optimization and ensemble average diagnostics.

2.6 LLM-Augmented Mechanism and Proxy Learning

Bidder/agent preferences in combinatorial auctions and similar domains are elicited using model-based proxies that combine DNF proper-learning machinery with LLM-augmented dialog and valuation inference pipelines (Huang et al., 24 Jan 2025). Here, the LLM both generates informative natural-language queries and uses transcript context to fill in missing preference data, reducing the number of queries required to achieve efficient allocations by up to 5$\times$ relative to classical proper learning.

2.7 Automated Elicitation in Bayesian Networks and Educational Systems

Training-based elicitation in classical AI includes parameter estimation or structure discovery in Bayesian networks used for diagnostic or tutoring purposes (via MML clustering, constrained search, or case-based evaluation) (Nicholson et al., 2013). Such approaches supplement or refine expert-elicited structure with data-driven adjustments, typically yielding higher classification match and predictive accuracy.

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3. Experimental Protocols and Quantitative Outcomes

A range of protocols and quantitative criteria have been established to evaluate training-based elicitation across contexts:

Context	Core Procedure	Metrics
Capability elicitation	Fine-tuning on demos, RL/Preference Opt	Correctness, accuracy, transfer, sample-efficiency (Greenblatt et al., 2024, Hofstätter et al., 4 Feb 2025)
Rubric elicitation	Online pairwise comparison + RL	Win-rate, rubric growth, qualitative themes (Rezaei et al., 8 Oct 2025)
Preference/question	Forward/reverse LLM training, LoRA	BLEU/ROUGE, % unanswered/repetitive, concept-rank (Montazeralghaem et al., 13 Oct 2025)
Hybrid-label SFT	Cost-constrained grid, prompt-inject	Test accuracy, Pareto frontier, marginal gain (Mallen et al., 2024)
Normalizing flows	SGD on simulation-based loss	MMD, correlation error, convergence slope (Bockting et al., 2024)
Auction proxies	DNF+LLM with transcript inference	Welfare-efficiency, queries to threshold (Huang et al., 24 Jan 2025)
BN structure/params	MML search, EM, MLE	Classification match, accuracy, posterior prob. (Nicholson et al., 2013)

Critical findings include high sample efficiency (as little as one demo unlocks 80% of ceiling in code/math), robust cross-domain generalization under fine-tuning, empirical superiority of training-based over prompt-based methods for deeply hidden or adversarial capabilities, and systematic mitigation of reward hacking in rubric learning when criteria are dynamically expanded.

4. Limitations, Failure Modes, and Diagnostic Recommendations

Major limitations of training-based elicitation identified in the literature include:

• Dependence on demonstration quality: If fine-tuning data are poor, SFT fails regardless of quantity; RL cannot recover if the base policy is extremely weak or exploration is sparse (Greenblatt et al., 2024).
• Reward hacking and checklist gaming: Models may superficially satisfy evolving rubrics or preference queries without genuine capability, requiring human-in-the-loop audits and high-fidelity model/rubric evaluation (Rezaei et al., 8 Oct 2025).
• Cost-accuracy tradeoffs and regime shifts: The optimal balance between label quantity and quality is context-dependent, and practitioners should assess the regime using marginal accuracy curves (Mallen et al., 2024).
• Computational and inference overhead: Dynamic rubric extraction, multi-round fine-tuning, and simulation-based optimization may incur substantial computational cost, with returns diminishing after a small number of epochs/demos in many settings (Greenblatt et al., 2024, Rezaei et al., 8 Oct 2025).

Recommended best practices include integrating fine-tuning into capability evaluation pipelines, careful curation and external verification of demonstration data, leveraging prompt-injection to augment small high-quality sets, using RL-based bootstrapping for imperfect environments, running adversarial “organism” tests to calibrate assessment pipelines, and maintaining rigorous diagnostics for convergence, overfitting, and transfer.

5. Applications Across Domains

Training-based elicitation is deployed in diverse settings including:

• Safety and capability audits for LLMs (password/circuit-locked models, adversarial stress tests) (Greenblatt et al., 2024, Hofstätter et al., 4 Feb 2025)
• Rubric and evaluation alignment in long-form open-ended QA, science, and reasoning tasks (Rezaei et al., 8 Oct 2025)
• Preference elicitation in recommender systems (sequential/funnel question fine-tuning, SLIM-based greedy optimization) (Montazeralghaem et al., 13 Oct 2025, Proissl et al., 2024)
• Mechanism design in combinatorial auctions (hybrid LLM-proxy protocols) (Huang et al., 24 Jan 2025)
• Expert prior elicitation in Bayesian inference and simulation-based science (normalizing flows, simulation diagnostics) (Bockting et al., 2024)
• Interactive dialog and requirements gathering in education and engineering (LLM and robot-based interviewers, multi-phase feedback systems) (Görer et al., 2024, Lojo et al., 10 Mar 2025, Görer et al., 2023, Görer et al., 2023)
• Automated knowledge base extension and parameter learning in intelligent tutoring and diagnostic BNs (Nicholson et al., 2013)

6. Emerging Directions and Open Challenges

Cutting-edge research is investigating:

• Automated demonstration generation using advanced LLMs, enabling coverage of model capabilities exceeding those of humans (Greenblatt et al., 2024).
• Scaling dynamic rubric or criterion discovery with active sampling and preference modeling to further bridge the gap between explicit and implicit task objectives (Rezaei et al., 8 Oct 2025).
• Unsupervised or weakly-supervised elicitation using f-divergence maximization, mutual information, and contrastive learning (Wei et al., 2019, Bockting et al., 2024).
• Diagnostic pipelines integrating adversarial “locked/circuit-broken” model organisms to stress-test elicitation methods (Hofstätter et al., 4 Feb 2025).
• Human-in-the-loop confirmation and audit regimes to validate preference, rubric, or prior specification.

Persistent open challenges include eliciting capabilities or knowledge that are inherently unreachable for human evaluators, preventing sophisticated reward hacking as model alignment grows more dynamic, and developing principled, sample-efficient, and trustworthy mechanisms for scalable, cost-sensitive, and high-stakes settings.

7. Summary Table: Representative Training-Based Elicitation Methods

Elicitation Target	Methodology	Key References
LLM hidden capabilities	SFT, RL, circuit-breaking	(Greenblatt et al., 2024, Hofstätter et al., 4 Feb 2025)
Dynamic rubrics	Online RL, pairwise comp.	(Rezaei et al., 8 Oct 2025)
User/preference profiles	Diffusion-inspired FT	(Montazeralghaem et al., 13 Oct 2025)
Expert priors	Flow-based SGD fitting	(Bockting et al., 2024)
Auction preferences	DNF LLM proxy learning	(Huang et al., 24 Jan 2025)
Interview scripts	Prompt-chaining + retrieval	(Görer et al., 2024)
BN parameters/structure	MML clustering/learned CPT	(Nicholson et al., 2013)

Training-based elicitation constitutes a unifying paradigm across contemporary AI, statistics, and applied decision research—offering systematic protocols for uncovering, specifying, and aligning capabilities or knowledge that would remain inaccessible to static prompting, shallow querying, or observation alone. The empirical, mathematical, and algorithmic foundations of this paradigm continue to expand, with emphasis on sample efficiency, robustness under adversarial or cost-constrained regimes, and integration of human and model intelligence for scalable oversight and reliable discovery.