Understanding Helpfulness and Harmless Tension in Reward Models
Reward models (RMs) are a key component of reinforcement learning from human feedback, enabling the alignment of large language models with human values such as helpfulness and harmlessness. However, these alignment goals can conflict, leading to interference when optimised jointly. We refer to this conflict as alignment tension and investigate how it manifests in RMs. To this end, we train RMs optimised for helpfulness, harmlessness, and a combined helpful-harmless objective, and compare their behaviours and internal representations. We find that mixed-objective RMs frequently underperform their single-objective counterparts, suggesting substantial interference between alignment goals. U...
| Authors | Eshaan Tanwar, Pepa Atanasova |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 13 October 2026 |
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