Every Question Has Its Own Value: Reinforcement Learning with Explicit
  Human Values
Authors: name: Dian Yu, name: Yulai Zhao, name: Kishan Panaganti, name: Linfeng Song, name: Haitao Mi, name: Dong Yu
Organization: Tencent

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.