Process Reinforcement through Implicit Rewards
Dense process rewards have proven a more effective alternative to the sparse
outcome-level rewards in the inference-time scaling of large language models
(LLMs), particularly in tasks requiring complex multi-step reasoning. While
dense rewards also offer an appealing choice for the reinforcement learning
(RL) of LLMs since their fine-grained rewards have the potential to address
some inherent issues of outcome rewards, such as training efficiency and credit
assignment, this potential remains largely unrealized. This can be primarily
attributed to the challenges of training process reward models (PRMs) online,
where collecting high-quality process labels is prohibitively expensive, making
them particularly vulnerable to reward hacking. To address these challenges, we
propose PRIME (Process Reinforcement through IMplicit rEwards), which enables
online PRM updates using only policy rollouts and outcome labels through
implict process rewards. PRIME combines well with various advantage functions
and forgoes the dedicated reward model training phrase that existing approaches
require, substantially reducing the development overhead. We demonstrate
PRIME's effectiveness on competitional math and coding. Starting from
Qwen2.5-Math-7B-Base, PRIME achieves a 15.1% average improvement across several
key reasoning benchmarks over the SFT model. Notably, our resulting model,
Eurus-2-7B-PRIME, surpasses Qwen2.5-Math-7B-Instruct on seven reasoning
benchmarks with 10% of its training data.
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Process Reinforcement through Implicit Rewards
Dense process rewards have proven a more effective alternative to the sparse
outcome-level rewards in the inference-time scaling of large language models
(LLMs), particularly in tasks requiring complex multi-step reasoning. While
dense rewards also offer an appealing choice for the reinforcement learning
(RL) of LLMs since their fine-grained rewards have the potential to address
some inherent issues of outcome rewards, such as training efficiency and credit
assignment, this potential remains largely unrealized. This can be primarily
attributed to the challenges of training process reward models (PRMs) online,
where collecting high-quality process labels is prohibitively expensive, making
them particularly vulnerable to reward hacking. To address these challenges, we
propose PRIME (Process Reinforcement through IMplicit rEwards), which enables
online PRM updates using only policy rollouts and outcome labels through
implict process rewards. PRIME combines well with various advantage functions
and forgoes the dedicated reward model training phrase that existing approaches
require, substantially reducing the development overhead. We demonstrate
PRIME's effectiveness on competitional math and coding. Starting from
Qwen2.5-Math-7B-Base, PRIME achieves a 15.1% average improvement across several
key reasoning benchmarks over the SFT model. Notably, our resulting model,
Eurus-2-7B-PRIME, surpasses Qwen2.5-Math-7B-Instruct on seven reasoning
benchmarks with 10% of its training data.
PRIME leverages implicit process rewards to improve the reinforcement learning of large language models, achieving better performance with less data compared to traditional methods.
dense process rewards sparse outcome-level rewards large language models reinforcement learning process reward models reward hacking policy rollouts advantage functions Qwen2.5-Math-7B-Base reasoning benchmarks Eurus-2-7B-PRIME