Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
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.
68e8722c95e8e6771df38987
Leitian Tao
claimed_verified
2025-10-10T04:32:16.240Z
655eca47e1b6d15cfe8f8235
/avatars/1ab847d12957a8ab77c84bdc452e3f5f.svg
Tao
user
Leitian
68e8722c95e8e6771df38988
Ilia Kulikov
68e8722c95e8e6771df38989
Swarnadeep Saha
68e8722c95e8e6771df3898a
Tianlu Wang
68e8722c95e8e6771df3898b
Jing Xu
68e8722c95e8e6771df3898c
Yixuan Li
68e8722c95e8e6771df3898d
Jason E Weston
68e8722c95e8e6771df3898e
Ping Yu
Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
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.
HERO, a reinforcement learning framework, combines verifier signals with reward-model scores to enhance reasoning in large language models, outperforming both RM-only and verifier-only methods.
verifiable rewards deterministic checkers binary feedback reward models continuous feedback reinforcement learning HERO Hybrid Ensemble Reward Optimization stratified normalization variance-aware weighting mathematical reasoning benchmarks