Human-Feedback Weighting Approaches

• Human-feedback weighting is a method for assigning scalar importance values to human signals, managing noise, inconsistency, and adversarial feedback in RLHF.
• Techniques include explicit scalar coefficients, learned predictors, Bayesian and latent-variable models, and dynamic online weight updates to enhance model robustness.
• Empirical results show that integrating feature-based and adaptive weighting significantly improves feedback prediction accuracy and policy performance in reinforcement learning tasks.

Human-feedback weighting is the assignment, prediction, or adaptive learning of scalar importance parameters to human-generated signals within learning algorithms—primarily in the context of reinforcement learning from human feedback (RLHF), preference modeling, and reward aggregation. The goal is to modulate the influence of individual feedback signals, labelers, or aggregated ratings so as to optimize data efficiency, prediction quality, robustness to feedback heterogeneity and uncertainty, and incentive compatibility, especially when feedback is noisy, inconsistent, or adversarial. Research on human-feedback weighting spans explicit scalar coefficients, learned or personalized importance weights, latent-variable models, Bayesian and game-theoretic frameworks, and sophisticated aggregation methods tuned to feedback granularity.

1. Formal Models and Taxonomy of Human-Feedback Weighting

Human-feedback weighting is realized via several mechanisms:

• Explicit Scalar Weights: Multiplicative coefficients on individual feedback signals (e.g., $w_i$ for the ith labeler or feedback sample).
• Learned Feedback Predictors: Models predicting the reliability or informativeness of human feedback based on context, annotator features, or task-level priors, which output weights for downstream learning (Fang et al., 16 Jun 2025).
• Latent-variable/Uncertainty Models: Frameworks in which latent correctness or annotator ability is estimated and the corresponding likelihoods drive adaptive weighting (He et al., 2020).
• Aggregated-Aggregator Models: Dynamic schemes updating weights for aggregating feedback from multiple, possibly strategic, annotators (Hao et al., 22 Dec 2024, Park et al., 30 Apr 2024).
• Granularity-aware Aggregation: Techniques exploiting granularity of categorical feedback, ranging from regularized averaging to Bayesian shrinkage, hierarchical models, and supervised-learned aggregation (Kagrecha et al., 16 Jul 2025).
• Personalization: Annotator- or group-specific weights, including feature-level importance or learned individualized reward heads (Movva et al., 30 Oct 2025, Park et al., 30 Apr 2024).

These schemes operate in both online and offline regimes, under both centralized and decentralized RL, and are applicable with pairwise, scalar, or probability-distribution feedback.

2. Predictive Weighting from Annotator Attributes and Signals

The CHARM model exemplifies end-to-end prediction of human feedback and its weighting conditioned on rich annotator feature vectors and task context (Fang et al., 16 Jun 2025). Given:

• Human features $x_h$ (trust in robots, robot experience, educational background, prior teaching, teaching style, and Big-Five personality; $D_h \approx 28$),
• Task features $x_t$ (e.g., cumulative reward for trajectory segment),

a joint classifier-regressor MLP predicts the likely feedback label and response latency. The output is mapped to a scalar weight $w = |ŷ| / 2$, such that extreme ratings (|ŷ|=2) yield maximum weight, while neutral or low-confidence feedback yields lower weight. Alternatively, weight can be set to the softmax probability for the predicted class.

Empirically, incorporating human characteristics improved five-class feedback prediction accuracy from 32.59% (reward only) to 55.83% (reward + annotator features), and binary feedback accuracy from 79.35% to 87.03%. This suggests feature-based feedback weighting isolates more informative (and less noisy) human labels, which substantially enhances the effectiveness of RLHF sample weighting.

3. Adaptive and Incentive-Compatible Weighting under Heterogeneity

Strategic annotator behavior and preference heterogeneity require adaptive weighting for both efficiency and incentive alignment (Hao et al., 22 Dec 2024, Park et al., 30 Apr 2024). For multi-labeler RLHF, dynamic multiplicative-weight updates are used:

$$w_i^{t+1} = w_i^{t} (1 - \alpha \ell_i^t), \qquad \ell_i^t = \frac{1}{m_t} \sum_{j=1}^{m_t} (\hat P_i^j - p_j^t)^2$$

with $\alpha$ a step-size. This protocol provably incentivizes truthful reporting (i.e., labelers maximize expected weight by reporting actual beliefs), bounds regret by $O(\sqrt{T\log N})$, and quickly downweights inaccurate or adversarial labelers.

Aggregative models further structure human-feedback weighting via:

• Utilitarian–Leximin Aggregation: Scalar parameter $\alpha$ interpolates between mean and minimum reward over labelers:

$$\text{Agg}_\alpha(r_1,\ldots,r_N) = \frac{1}{\alpha} \log\left(\frac{1}{N} \sum_{i=1}^{N} e^{\alpha r_i}\right)$$

with the Leximin ($\alpha \rightarrow -\infty$) limiting case maximizing minimum utility (Park et al., 30 Apr 2024).

• Mechanism Design: Proper scoring rules and Vickrey–Clarke–Groves-like payments ensure dominant-strategy truthfulness even under probabilistic-opinion feedback, maximizing social welfare in reward aggregation.

4. Probabilistic and Uncertain Human Feedback: EM and Bayesian Weighting

Probabilistic feedback models explicitly account for uncertainty in human responses (He et al., 2020). The core model posits, for each state–action pair, the probability of positive, negative, or null feedback as a function of action’s proximity to the trainer’s (latent) preferred action:

$$P(f = f^+ | a, s, \lambda(s), \theta) = \mu^+ \exp\left( -\frac{(a - \lambda(s))^2}{2\sigma^2} \right)$$

$$P(f = f^- | a, s, \lambda(s), \theta) = \mu^- [1 - (1-\epsilon) \exp\left(-\frac{(a - \lambda(s))^2}{2\sigma^2}\right)]$$

$P(f = f^0 | \cdots) = 1 - P(f^+) - P(f^-)$

Learning proceeds by alternating (i) “E-step” updates of the latent preferred action with current uncertainty, using feedback-likelihood-weighted updates, and (ii) gradient steps for uncertainty parameter ($\sigma$). This combines latent responsibility weighting with explicit adaptation of model uncertainty. Empirically, such models achieve faster convergence and greater robustness compared to non-weighted (UCB) or fixed-kernel approaches.

5. Aggregation in High-Granularity Feedback and Supervised Weight Learning

When collecting k-ary (e.g., Likert, 5- or 11-point) ratings, simple regularized averaging becomes sub-optimal as granularity grows (Kagrecha et al., 16 Jul 2025). Under a regularized Bayesian framework, the empirical category frequencies $\hat{p}^{emp}$ are combined with a prior $p^0$ using pseudocounts ($\alpha$):

$$\hat{p}^{reg} = \frac{n \hat{p}^{emp} + \alpha p^0}{n + \alpha}$$

However, supervised learned-aggregation, e.g., by training an MLP to map input ratings to output population PMFs, can halve sample complexity for k=5 or k=11 relative to regularized averaging. Hierarchical Bayesian and Stein-shrinkage estimators further exploit individual- and item-specific reliability, enabling weight adaptation per rater and per granularity level.

Empirical analyses confirm that as granularity increases, supervised or hierarchical Bayesian aggregation methods substantially outperform regularized averaging in terms of loss and required number of raters.

6. Weighting in Reward Learning and Robust RLHF

Reward modeling in RLHF pipelines is highly sensitive to policy-induced distribution shift. OCRM (Off-Policy Corrected Reward Modeling) introduces an explicit importance weighting mechanism to correct the reward model’s empirical loss during policy iteration (Ackermann et al., 21 Jul 2025):

$$w(s,a_w,a_l) = \frac{\pi^{(i)}(a_w|s) \pi^{(i)}(a_l|s)}{\pi^{(1)}(a_w|s) \pi^{(1)}(a_l|s)}$$

Reward model retraining applies these weights to the cross-entropy loss, ensuring consistency with the current policy distribution and preventing reward hacking or overoptimization (“Goodhart’s Law”). The empirical effect is up to +10% improvement in policy win rate on RLHF benchmarks, establishing off-policy weighting as crucial for maintaining alignment under online policy adaptation.

Proportional weighting also surfaces in GFlowHF (Li et al., 2023), which enforces that the marginal policy over final states is proportional to learned (human-scored) reward via explicit flow-matching losses—substantially improving diversity and robustness to noisy labels relative to reward-maximization RLHF.

7. Personalized, Interpretable, and Feature-Level Feedback Weighting

Interpretable human-feedback weighting is addressed by sparse autoencoder and mixed-effects modeling (Movva et al., 30 Oct 2025). WIMHF factorizes preference signals into interpretable feature axes with per-feature and per-annotator weights:

• Sparse Encoders: Project embedding-difference features into a few latent, human-identifiable dimensions; per-feature logistic regression coefficients ($\beta_j$) quantify impact on preference labeling.
• Annotator-Specific Weights: Mixed-effects models allocate per-user slopes on selected features, enabling fine-grained personalization while guarding against overfitting and data inefficiency.

Personalization in RLHF is formalized by representation- or clustering-based approaches assigning per-user (or per-cluster) reward heads and learning them via MLE or robust confidence sets, then solving robust RL under individual or cluster-specific models (Park et al., 30 Apr 2024). Aggregation-based approaches maintain a single reward model but interpolate between utilitarian and Leximin pooling to balance majority and minority preferences, with explicit mechanism-design safeguards for incentive compatibility.

Summary Table: Core Human-Feedback Weighting Schemes

Weighting Technique	Key Mechanism/Param	Representative Reference
Scalar/Manual Weighting	α, explicit weights	(Mathewson et al., 2016, Mathewson et al., 2017)
MLP-predicted Feedback Weights	f_θ(xₕ, xₜ),	ŷ
Adaptive/Multiplicative Weights	Online w_i update	(Hao et al., 22 Dec 2024, Park et al., 30 Apr 2024)
Probabilistic Latent-Variable Models	p⁺, p⁻, σ, EM/ABL-UF	(He et al., 2020)
Bayesian/Supervised Aggregation	Dirichlet/MLP/SL	(Kagrecha et al., 16 Jul 2025)
Off-Policy Importance Weighting	Ratio πᶦ/π¹	(Ackermann et al., 21 Jul 2025)
Feature-based/Personalization	βj (feature), δ{a,j}	(Movva et al., 30 Oct 2025, Park et al., 30 Apr 2024)

Concluding Perspectives

Human-feedback weighting is fundamental to the reliability, efficiency, and integrity of RLHF, reward modeling, and preference-based learning. Recent research demonstrates that principled weighting—whether via annotator attribute-based predictors, dynamic online aggregation, probabilistic uncertainty modeling, distribution-aware corrections, or interpretable feature attribution—yields substantial improvements in both downstream model performance and robustness. A plausible implication is that future RLHF pipelines should treat human-feedback weighting as a first-class design parameter, with explicit schemes for both annotation-level and feature-level weighting, adaptive tuning in online settings, and comprehensive tooling for bias, variance, and incentive trade-off management.