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README.md

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 -  [Blog](https://curvy-check-498.notion.site/Process-Reinforcement-through-Implicit-Rewards-15f4fcb9c42180f1b498cc9b2eaf896f)
 -  [GitHub](https://github.com/PRIME-RL/PRIME)
 
-# Introduction
-
-Advanced reasoning of large language models (LLMs), while improvable through data-driven imitation, is still clouded by serious scalability challenges. We believe the key to overcoming such challenges lies in transforming data-driven approaches into *exploration-based* methods, as exemplified by reinforcement learning (RL). To this end, two critical bottlenecks need to be alleviated to bridge this transformation: (1) how to obtain precise *reward signals* efficiently and scalably, especially for *dense* ones? (2) how can we build effective RL algorithms to fully *unleash* the potential of these signals? 
-
-
-We seek the **scalable** path towards advanced reasoning capabilities with **efficient reward modeling and reinforcement learning**. Our work stems from the implicit process reward modeling (PRM) objective. Without the need for any process label, implicit PRM is trained as an outcome reward model (ORM) and then used as a PRM. Besides improving model performance through inference scaling, the true power of the implicit PRM is unveiled in online RL training. Specifically, it brings three benefits to RL:
-- **Dense Reward:** Implicit PRM directly learns a Q-function that provides rewards for *each token*, which alleviates the reward sparsity issue without the need of an extra value model.
-- **Scalability:** Implicit PRM can be online updated with only outcome label. Therefore, we can directly update the PRM with on-policy rollouts given outcome verifiers, which mitigates the distribution shift as well as scalability issues for PRMs.
-- **Simplicity:** Implicit PRM is inherently a language model. In practice, we show that it is unnecessary to train a PRM beforehand, since the SFT model itself already serves as a strong starting point.
-
-We then dive into RL to figure out its key algorithm designs and implementation techniques. To this end, we present Process Reinforcement through IMplicit rEwards, PRIME, which effectively incorporates and updates PRMs in RL. 
-
-
-# Usage
-We apply tailored prompts for coding and math task:
-**System Prompt**
-```
-\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n
-```
-**Coding**
-```
-{question} + "\n\nWrite Python code to solve the problem. Present the code in \n```python\nYour code\n```\nat the end.
-```
-**Math**
-```
-{question} + "\n\nPresent the answer in LaTex format: \\boxed{Your answer}"
-```
 # Evaluation
 Through PRIME, we successfully achieve substantial improvements on key reasoning benchmarks over our SFT version of the model, leading to **16.7%** improvement on average, and over **20%** on AMC&AIME competitions. Our final model Eurus-2-7B-PRIME, based on Qwen-2.5-Math-7B-Base, surpassed its instruct version on 5 key reasoning benchmarks. 
 The final results are presented below: