Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
Authors: name: Leitian Tao, name: Ilia Kulikov, name: Swarnadeep Saha, name: Tianlu Wang, name: Jing Xu, name: Yixuan Li, name: Jason E Weston, name: Ping Yu
Organization: AI at Meta

Post-training for reasoning of large language models (LLMs) increasingly
relies on verifiable rewards: deterministic checkers that provide 0-1
correctness signals. While reliable, such binary feedback is brittle--many
tasks admit partially correct or alternative answers that verifiers
under-credit, and the resulting all-or-nothing supervision limits learning.
Reward models offer richer, continuous feedback, which can serve as a
complementary supervisory signal to verifiers. We introduce HERO (Hybrid
Ensemble Reward Optimization), a reinforcement learning framework that
integrates verifier signals with reward-model scores in a structured way. HERO
employs stratified normalization to bound reward-model scores within
verifier-defined groups, preserving correctness while refining quality
distinctions, and variance-aware weighting to emphasize challenging prompts
where dense signals matter most. Across diverse mathematical reasoning
benchmarks, HERO consistently outperforms RM-only and verifier-only baselines,
with strong gains on both verifiable and hard-to-verify tasks. Our results show
that hybrid reward design retains the stability of verifiers while leveraging
the nuance of reward models to advance reasoning.