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Every Question Has Its Own Value: Reinforcement Learning with Explicit Human Values

Dian Yu, Yulai Zhao, Kishan Panaganti, Linfeng Song, Haitao Mi, Dong Yu

arXiv:2510.20187v1cs.LGcs.CL

TL;DR

Correctness-only rewards overlook that prompts can have different human-assigned importance, even in objective tasks. RLEV incorporates those values into the reward, and experiments report stronger value-weighted performance than correctness-only baselines alongside value-sensitive response lengths.

  • Problem

    RLVR assigns the same correctness reward to every prompt, although human objectives can value correct answers differently and may depend on total score rather than answer count.

    Source: §1 · p. 2

  • Method

    RLEV extends RLVR by incorporating explicit prompt values into correctness rewards, with utility determined jointly by correctness and prompt value.

    Source: §1 · p. 2, §2.1 · p. 3

  • Results

    RLEV consistently outperforms correctness-only baselines, improving value-weighted accuracy and producing more concise responses, with value-scaled updates linked to end-of-sequence tokens.

    Source: §1 · p. 2, §5 · p. 11

  • Takeaways & Limitations

    Explicit, non-uniform utility optimization is a practical step toward aligning LLM behavior with human priorities in settings where importance is quantified.

    Source: §2.1 · p. 3, §6 · p. 11

    The framework represents human value as a single predefined scalar suited to objective domains where importance is explicitly quantified.

    Source: §6 · p. 11

Abstract

from arXiv · show

We propose Reinforcement Learning with Explicit Human Values (RLEV), a method that aligns Large Language Model (LLM) optimization directly with quantifiable human value signals. While Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains models in objective domains using binary correctness rewards, it overlooks that not all tasks are equally significant. RLEV extends this framework by incorporating human-defined value signals directly into the reward function. Using exam-style data with explicit ground-truth value labels, RLEV consistently outperforms correctness-only baselines across multiple RL algorithms and model scales. Crucially, RLEV policies not only improve value-weighted accuracy but also learn a value-sensitive termination policy: concise for low-value prompts, thorough for high-value ones. We demonstrate this behavior stems from value-weighted gradient amplification on end-of-sequence tokens. Ablation studies confirm the gain is causally linked to value alignment. RLEV remains robust under noisy value signals, such as difficulty-based labels, demonstrating that optimizing for an explicit utility function offers a practical path to aligning LLMs with human priorities.

1 Introduction

Correctness-only rewards treat every prompt as equally important, although human objectives may assign different values to correct answers. RLEV incorporates explicit prompt values into rewards and improves value-weighted performance while producing value-sensitive response lengths.

1 Introduction

  • RLVR rewards every correct answer uniformly, so maximizing correct-answer counts can diverge from maximizing total value, such as an exam score.A 10-point question is more valuable than a 2-point question.Source: §1 · p. 2
  • RLEV extends RLVR by scaling correctness rewards with explicit, human-assigned prompt values.Its utility depends jointly on response correctness and the prompt’s intrinsic value.Source: §1 · p. 2
  • Value-scaled rewards amplify end-of-sequence updates, accompanying concise answers to low-value prompts and thorough answers to high-value prompts.Ablations link gains to alignment with human-defined values rather than reward magnitude; performance also remains above baseline with noisy value signals.Source: §1 · p. 2
  • RLEV outperforms correctness-only baselines across multiple RL algorithms and model scales, improving value-weighted scores and response conciseness.The experiments use exam-style examples with ground-truth value labels.Source: §1 · p. 2

2 Method: Learning from Human-Aligned Rewards

RLEV formalizes response utility as prompt value combined with verifiable correctness, then uses that utility to define reinforcement-learning rewards and optimize expected utility.

2.1 A Human Utility Function for Valued and Verifiable Outcomes

  • Utility is prompt value multiplied by a correctness indicator: correct responses earn the question's value, while incorrect responses earn zero.The policy is trained to maximize expected utility.Source: §2.1 · p. 3

2.2 Normalizing Human Values

  • Normalized question value is its share of the exam's total score, bounding it between 0 and 1 as a measure of relative importance.Question scores are normalized because exams have different total scores.Source: §2.2 · p. 3

2.3 The RLEV Reward Function

  • RLEV's surrogate reward gives correct answers a value-scaled reward in [1, 2] and incorrect answers zero, preserving a minimum reward while giving high-value questions a bonus.The scale is based on normalized prompt value and is clipped to stabilize the learning signal.Source: §2.3 · p. 3

2.4 The Reinforcement Learning Objective

  • The RL objective maximizes expected cumulative reward over a prompt dataset, with nonzero reward only at the final step.The passage describes this as simplifying the objective.Source: §2.4 · p. 4

2.5 Gradient Derivation

  • Value scaling amplifies gradient magnitude; when EOS is more likely to yield correctness than continuing on average, it is reinforced more strongly.The analysis also says continuations below the policy-weighted average correctness receive negative gradients, supporting a tendency toward earlier termination.Source: §2.5 · p. 5

3 Experiments

RLEV is evaluated on exam-style data with human-labeled question values, using correctness and value-sensitive metrics. Across the tested configurations, it improves human-aligned accuracy and response efficiency, with gains also reported on out-of-distribution benchmarks.

3.1 Datasets

  • The exam dataset contains expert-answered question–answer pairs with human-labeled question scores; 100,000 training and 8,000 test examples are split by exam to avoid leakage.The original exam content is predominantly Chinese.Source: §3.1 · pp. 5–6

3.2 Experimental Setup

  • All policies train for one epoch on eight GPUs at a 5e-7 learning rate, with 128 rollouts per batch and Qwen2.5-7B or Qwen2.5-32B base-model initialization.Prompts and generated responses are capped at 1,024 tokens, and evaluation uses greedy decoding.Source: §3.2 · p. 6

3.3 Evaluation Metrics

  • Evaluation tracks unweighted accuracy, value-weighted H-Acc, response length, and value density, while Qwen2.5-72B-Instruct verifies semantic equivalence of final answers.Value density divides percentage H-Acc by average response length.Source: §3.3 · p. 6

3.4 RLEV with Ground-Truth Human Values

  • For 32B models, average response length falls from 246.9 to 98.6 tokens under RLEV.The learned policy is concise on low-value prompts while remaining thorough on high-value prompts.Source: §3.4 · p. 7
  • 2.0% and 2.8% average Human-Aligned Accuracy (H-Acc) gains for 7B and 32B models, respectively, show RLEV outperforming correctness-only training across tested configurations.The comparison covers RLEV and baseline policies at both model scales.Source: Table 1 · p. 7, §3.4 · p. 7
  • RLEV policies trained on Chinese data outperform correctness-only counterparts on several OOD English and Chinese benchmarks, including GPQA Diamond and SuperGPQA.The evaluation set also includes C-Eval and MMLU-Pro.Source: §3.1 · p. 6, Table 2 · p. 7, §3.4 · p. 7

3.5 RLEV with Other Types of Human Values

RLEV also performs with imperfect value signals and develops value-sensitive stopping behavior. Ablations and reward-function tests distinguish prompt-aligned scaling from generic reward scaling and assess design choices for the reward.

3.5 RLEV with Other Types of Human Values

  • Both difficulty-based weak labels and predictor-generated values outperform the correctness-only baseline on WebInstruct-verified, where ground-truth values may be unavailable.The predictor is trained on the main exam dataset.Source: Table 3 · p. 8, §3.5 · p. 8

3.6 Analysis of Value-Sensitive Termination

  • Figure 2 and token-level analysis show earlier EOS probability increases for low-value prompts but EOS suppression for high-value prompts, supporting concise versus thorough responses.The value-scaling factor amplifies the signal to continue when adding tokens has higher expected correctness than stopping.Source: §3.6 · p. 8, Figure 2 · p. 9

3.7 Ablation Studies: Isolating the Impact of Human Values

  • Only prompt-aligned reward scaling meaningfully increases H-Acc and reduces response length; uniform scaling degrades performance, while random weights do not improve conciseness.The controls test whether reward magnitude alone, rather than alignment to human-defined values, explains the gains.Source: §3.7 · p. 9, Table 4 · p. 10

3.8 Reward Function Sensitivity and Design

  • α = 10 offers the best balance of human-aligned accuracy and response conciseness across the tested settings.Performance is reported as robust across a range of α values.Source: Table 5 · p. 10, §3.8 · p. 10
  • The chosen additive and clipped reward form outperforms a purely multiplicative alternative; clipping limits excessively large rewards for the small fraction of high-value outliers.The value distribution has mean 0.02, and 1.18% of training examples have value above 0.1.Source: Table 6 · p. 10, §3.8 · p. 10

4 Related Work

Prior work weights learning signals or enriches verifiable rewards, but the cited approaches do not address human-defined per-prompt importance in the same way. RLEV incorporates those values into RLVR and studies the resulting behavior and mechanisms.

4 Related Work

  • Classical weighting methods adjust gradients to emphasize important samples, but the cited work does not consider human-defined real-world values attached to individual prompts or questions.Examples include importance-weighted transfer, reward-weighted regression, and advantage-weighted regression.Source: §4 · p. 11
  • LLM verifiable-reward work modifies reward form or composition, but the cited approaches do not explicitly scale correctness rewards by normalized human-assigned per-prompt values or analyze resulting gradient mechanisms.Examples include ConfClip, rubric rewards, and RLCR with calibration rewards.Source: §4 · p. 11
  • RLEV integrates human-assigned per-prompt importance into RLVR with a clipped scaling surrogate, and evaluates it through empirical tests, ablations, and gradient analysis.The paper reports more concise and human-aligned behavior from optimizing explicit, value-weighted utility.Source: §4 · p. 11

5 Conclusions and Future Work

RLEV scales correctness rewards by explicit value signals and improves value-weighted accuracy while producing more concise responses. The paper links conciseness to value-weighted amplification of end-of-sequence gradients and identifies noisy-value robustness and dynamic values as future directions.

5 Conclusions and Future Work

  • RLEV improves value-weighted accuracy over correctness-only baselines and produces more concise responses.The conclusion describes scaling correctness rewards with an explicit value signal.Source: §5 · p. 11
  • Value-weighted gradient amplification on end-of-sequence tokens explains the observed conciseness.Source: §5 · p. 11
  • Ablations link the gains to value alignment rather than reward magnitude, and RLEV also outperforms baselines with noisy difficulty-derived values.Source: §5 · p. 11
  • Future work could explore value functions that adapt to user priorities or combine RLEV with RLHF for subjective qualities such as style and tone.Source: §5 · p. 11

6 Broader Impact and Limitations

The framework is presented as effective for aligning LLM behavior with human priorities, but its value formulation and data requirements constrain its scope. It uses a static value function and does not represent broader human values as dynamic, multidimensional, or subjective.

6 Broader Impact and Limitations

  • RLEV represents human value as a single, predefined scalar, suited to objective domains where importance is explicitly quantified.Source: §6 · p. 11
  • Applying RLEV requires explicit value labels for each prompt, even though experiments show robustness to noisy signals.Source: §6 · p. 11
  • The current method uses a static value function; adapting values to user priorities in real time is left for future work.Source: §6 · p. 11

A.1 Data Statistics

The data statistics describe normalized, per-exam proportional values for 100k training instances and 8k testing instances. Figures 3 and 4 show the value distributions for the respective subsets.

A.1 Data Statistics

  • 100k training instances and 8k testing instances have normalized values defined as per-exam proportional scores.Source: §A.1 · p. 14
  • Figure 3 shows the distribution of ground-truth human-defined normalized values in the 100k training examples.Source: Figure 3 · p. 14, §A.1 · p. 14

A.2 Score Prediction

A score predictor generates pseudo-values for datasets without ground-truth scores, with exam data formatted for supervised fine-tuning and consistent total scores used for WebInstruct-verified. The predictor's reported exact-match accuracy and score correlation characterize its performance; Figure 4 shows the test-set value distribution.

A.2 Score Prediction

  • The predictor generates pseudo-values for datasets where ground-truth scores are unavailable, using rule-based difficulty scores as another imperfect signal.Source: §A.2 · p. 14
  • Exam data are formatted as in Table 7 and used for two-epoch supervised fine-tuning with Qwen2.5-7B; WebInstruct-verified prompts receive a standardized total score of 100.Source: §A.2 · p. 14, Table 7 · p. 14
  • Figure 4 shows the distribution of ground-truth human-defined normalized values in the 8k test examples.Source: Figure 4 · p. 15
  • 79.5% exact-match accuracy and Pearson correlation 0.91 (p < 0.001) are reported for the score predictor.Source: §A.2 · p. 15

A.3 Detailed Accuracy Analysis

RLEV has higher accuracy than the correctness baseline in every high-valued bin and nearly every low-valued bin; its gains are generally more pronounced on high-valued prompts.

A.3 Detailed Accuracy Analysis

  • Table 8 compares RLEV and correctness-reward policies across 7B and 32B models, including accuracy by value group.Source: Table 8 · p. 15
  • RLEV achieves higher accuracy than the correctness baseline in all high-valued bins, with improvements generally more pronounced there.The comparison covers the top 20% high-valued prompts.Source: §A.3 · p. 15, Table 8 · p. 15
  • RLEV achieves higher accuracy than the correctness baseline in nearly all low-valued bins.The comparison covers the bottom 20% low-valued prompts.Source: §A.3 · p. 15, Table 8 · p. 15
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