GRPO Reinforcement Learning Post-Training: Enabling Qwen2.5-14B to Independently Discover Complex Reasoning Paths

This article introduces the open-source project RLVR_GRPO, which implements the novel reinforcement learning method Group Relative Policy Optimization (GRPO) for post-training the Qwen2.5-14B model. Through verifiable reward functions, it enables the model to independently learn and optimize complex reasoning abilities, addressing the limitations of traditional supervised fine-tuning (SFT) and PPO methods in reasoning training.

Current large language models have limitations in complex reasoning. Traditional SFT methods tend to make models "memorize answers" rather than truly master reasoning. Traditional RL methods like PPO face issues such as sparse rewards and unstable training; value network training is difficult, and estimation errors affect policy updates.

GRPO is a reinforcement learning algorithm for language models. Its core is to estimate the advantage function through intra-group relative comparison, getting rid of dependence on value networks: 1. Group sampling mechanism (sampling multiple answers per question); 2. Relative advantage estimation (calculating advantages using relative reward values within the group); 3. Clipping the objective function to prevent excessive updates. Verifiable rewards (RLVR) have advantages such as immediacy, objectivity, and low cost, making them suitable for tasks with clear correctness standards like mathematics and code.

Qwen2.5-14B was chosen as the base model (moderate scale, strong basic capabilities, multilingual support, open weights). The training process includes data preparation (verifiable problem sets for mathematics/code, etc.), group sampling, reward calculation (validators like Python interpreter), advantage estimation (intra-group reward normalization), policy update, and iterative training. Key technical points: KL divergence constraint (to prevent deviation from the base model), temperature annealing (to balance exploration and exploitation), gradient accumulation (to simulate large batches).

After training, the model showed significant improvement in reasoning: self-discovered reasoning strategies (chain-of-thought, self-verification, strategy adjustment, reflection ability); typical behavior patterns (problem decomposition, hypothesis testing, backtracking correction, multi-path exploration). These abilities emerged autonomously through reinforcement learning, not through explicit programming.

In education, it can be used for personalized tutoring, step-by-step explanations, and adaptive exercises. In research, it can assist in literature analysis (extracting and verifying mathematical derivations), experimental design (proposing verifiable hypotheses), and code review (checking the correctness of scientific computing code).

Future expansion directions include multi-modal GRPO (combining text/images/code), tool usage (calling external tools to assist reasoning), multi-agent collaboration (collaboration of specialized models), and continuous learning (improving from new verification feedback).

GRPO has limitations: challenges in reward design (difficulty defining verification rules for open-ended tasks), low exploration efficiency (high sample cost), insufficient generalization ability (poor performance on out-of-distribution tasks), and safety risks (possible reward hacking leading to incorrect outputs).