PRIME: Enhancing Reasoning in Large Language Models with Implicit Rewards

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From Implicit Rewards to Improved Reasoning: A Look at PRIME
Scaling large language models (LLMs) for complex tasks, especially those requiring multi-step reasoning, presents a challenge. Traditionally, rewards based on the final result have often been used for this purpose. However, recent research suggests that so-called "dense process rewards," i.e., rewards given throughout the entire process, can be more effective. These detailed rewards also offer advantages for reinforcement learning (RL) of LLMs, as they have the potential to improve issues like training efficiency and credit assignment. However, this potential has not yet been fully realized due to the difficulties in online training of Process Reward Models (PRMs).
The challenge lies in obtaining high-quality process labels, which are essential for training PRMs. The collection of these labels is often very time-consuming and expensive. In addition, there is the risk of "reward hacking," where the model learns to maximize the rewards without actually solving the task.
To address these challenges, PRIME (Process Reinforcement through IMplicit rEwards) was developed. PRIME enables online updates of PRMs using policy rollouts and outcome labels through implicit process rewards. PRIME's innovative approach is to dispense with explicit process labels and instead derive implicit rewards from the learning process itself. This significantly simplifies training and reduces development effort, as no dedicated training phase for the reward model is required.
PRIME also combines well with various advantage functions, which can further improve the performance of the RL algorithm. Advantage functions quantify the advantage of a particular action compared to other possible actions in a given state.
The effectiveness of PRIME has been demonstrated in demanding areas such as mathematics and programming. Starting from Qwen2.5-Math-7B-Base, PRIME achieved an average improvement of 15.1% across various key reasoning benchmarks compared to the SFT (Supervised Fine-Tuning) model. Particularly noteworthy is that the resulting model, Eurus-2-7B-PRIME, surpasses Qwen2.5-Math-7B-Instruct on seven reasoning benchmarks – with only 10% of the training data.
These results highlight the potential of PRIME to advance the development of more powerful LLMs through more efficient and scalable reinforcement learning. The ability to forgo explicit process labels opens new avenues for training complex language models and could lead to significant advancements in areas such as automated reasoning, problem-solving, and decision-making.
For companies like Mindverse, which specialize in developing AI-powered solutions, these advancements in reinforcement learning are of great importance. The development of customized chatbots, voicebots, AI search engines, and knowledge systems benefits from more efficient and powerful language models. PRIME and similar approaches could help enable the next generation of AI applications and push the boundaries of what's possible in the field of artificial intelligence.
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