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

• URL: http://arxiv.org/abs/2510.20187v1
• Date: Thu, 23 Oct 2025 04:15:22 GMT
• Title: Every Question Has Its Own Value: Reinforcement Learning with Explicit Human Values
• Authors: Dian Yu, Yulai Zhao, Kishan Panaganti, Linfeng Song, Haitao Mi, Dong Yu,
• Abstract summary: We propose Reinforcement Learning with Explicit Human Values (RLEV)<n>RLEV aligns Large Language Model (LLM) optimization directly with quantifiable human value signals.<n>We show RLEV consistently outperforms correctness-only baselines across multiple RL algorithms and model scales.
• Score: 53.72318444646282
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment [24.492954219955788]
  We propose a closed-loop framework designed to navigate the trade-off between fine-tuning and Aligning Large Language Models (LLMs)<n> VISA features a high-precision value detector, a semantic-to-value translator, and a core value-rewriter.<n>Our experiments demonstrate that this approach enables precise control over a model's value expression while maintaining its factual consistency and general capabilities.
  arXiv  Detail & Related papers  (2026-03-05T05:12:26Z)
• Reinforcement Learning from Meta-Evaluation: Aligning Language Models Without Ground-Truth Labels [2.757286637005573]
  Reinforcement Learning from Meta-Evaluation (RLME)<n>We introduce RLME, which optimize a generator using reward derived from an evaluator's answers to natural-language meta-questions.<n>Across a suite of experiments, we show that RLME achieves accuracy and sample efficiency comparable to label-based training.
  arXiv  Detail & Related papers  (2026-01-29T05:02:08Z)
• From Verifiable Dot to Reward Chain: Harnessing Verifiable Reference-based Rewards for Reinforcement Learning of Open-ended Generation [52.62655622099456]
  We propose reinforcement learning with verifiable reference-based rewards (RLVRR)<n>Instead of checking the final answer, RLVRR extracts an ordered linguistic signal from high-quality references (i.e., reward chain)<n>In this way, RLVRR decomposes rewards into two dimensions: content, which preserves deterministic core concepts, and style, which evaluates adherence to stylistic properties.
  arXiv  Detail & Related papers  (2026-01-26T14:39:58Z)
• Reinforcement Learning on Pre-Training Data [55.570379963147424]
  We introduce Reinforcement Learning on Pre-Training data (R), a new training-time scaling paradigm for optimizing large language models (LLMs)<n>R enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL)<n>Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of R.
  arXiv  Detail & Related papers  (2025-09-23T17:10:40Z)
• ConfClip: Confidence-Weighted and Clipped Reward for Reinforcement Learning in LLMs [32.13266235550995]
  Reinforcement learning (RL) has become a standard paradigm for refining large language models (LLMs)<n>Inspired by observations from human learning, we introduce a RL technique that integrates verifiable outcomes with the model's own confidence estimates.
  arXiv  Detail & Related papers  (2025-09-22T13:00:35Z)
• Rethinking Reasoning Quality in Large Language Models through Enhanced Chain-of-Thought via RL [19.659532349434418]
  Reinforcement learning (RL) has recently become the dominant paradigm for strengthening the reasoning abilities of large language models.<n>Yet the rule-based reward functions commonly used on mathematical or programming benchmarks assess only answer format and correctness.<n>We propose Dynamic Reasoning Efficiency Reward (DRER) -- a plug-and-play RL reward framework that reshapes both reward and advantage signals.
  arXiv  Detail & Related papers  (2025-09-07T11:52:18Z)
• Learning to Reason without External Rewards [100.27210579418562]
  Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specific supervision.<n>We explore Reinforcement Learning from Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic signals without external rewards or labeled data.<n>We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal.
  arXiv  Detail & Related papers  (2025-05-26T07:01:06Z)
• Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? [34.18909976476456]
  We show that the distinguishing signal obtained in preferred responses is often concentrated in the early tokens.<n>Surprisingly, models trained on truncated datasets, retaining only the first half or fewer tokens, achieve comparable or even superior performance to those trained on full datasets.<n>We consider two simple decoding strategies motivated by the shallow reward signal observation, namely Length Control Decoding and KL Threshold Control Decoding, which leverage shallow preference signals to optimize the trade-off between alignment and computational efficiency.
  arXiv  Detail & Related papers  (2025-05-21T17:59:02Z)
• TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning [11.573904453859098]
  Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs)<n>Yet, RL's success relies on the reliability of rewards, which are provided by verifiers.<n>In this paper, we expose and analyze a widespread problem--false negatives--where verifiers wrongly reject correct model outputs.<n>We propose tinyV, a lightweight LLM-based verifier that augments existing rule-based methods.
  arXiv  Detail & Related papers  (2025-05-20T17:16:44Z)
• Lean and Mean: Decoupled Value Policy Optimization with Global Value Guidance [52.65461207786633]
  Policy-based Reinforcement Learning from Human Feedback is essential for aligning large language models with human preferences.<n>It requires joint training of an actor and critic with a pretrained, fixed reward model for guidance.<n>We propose textbfDecoupled Value Policy Optimization (DVPO), a lean framework that replaces traditional reward modeling with a pretrained emphglobal value model (GVM)
  arXiv  Detail & Related papers  (2025-02-24T08:11:33Z)
• VinePPO: Refining Credit Assignment in RL Training of LLMs [66.80143024475635]
  We propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates.<n>Our method consistently outperforms PPO and other baselines across MATH and GSM8K datasets in less wall-clock time.
  arXiv  Detail & Related papers  (2024-10-02T15:49:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.