Qwen 2.5 7B Math Model: Training Divergence Issues

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Qwen 2.5 7B Math Model: Training Divergence Issues

Hey everyone! I've been wrestling with a particularly nasty problem while training the Qwen 2.5 7B math model on the Math12K dataset, and I'm hoping some of you brilliant folks out there might have some insights. Specifically, I'm seeing severe divergence during training, and it's making my progress crawl to a halt. Let's dive into the nitty-gritty and see if we can figure out what's going on. I'm going to break down the problem, the steps I've taken, and hopefully, we can brainstorm some solutions together. Let's get started, shall we?

The Divergence Dilemma

So, the main issue is that the training process for my Qwen 2.5 7B model on the Math12K dataset isn't converging. Instead, the loss shoots up like a rocket, indicating that the model is completely unable to learn anything useful from the data. This is a common problem in deep learning, but it's particularly frustrating when you're working with a pre-trained model and a dataset specifically designed for math problems. The Math12K dataset should, in theory, be a great fit, so the divergence is a real head-scratcher. It's like the model is allergic to math! I'm seeing a classic case of PG loss going haywire. It's the kind of thing that makes you want to tear your hair out, am I right? What is going on here?

I've included some images to show you exactly what I mean. You can clearly see how the loss just explodes over the course of training iterations. This is not what you want to see! As you can see, the loss starts low but then skyrockets. You can see the loss graph. It's like the model is trying to solve the hardest math problems in the world all at once, and failing miserably. In my experiments, I disabled the KL divergence terms since current research suggests they have negligible impact (all other parameters remained unchanged). To be clear, the goal here is to get this model to learn and improve its performance on mathematical reasoning tasks. The Math12K dataset is a standard benchmark, so I was expecting some level of success. However, that didn't happen. One of the first things to check in these situations is the learning rate. Maybe it's too high, causing the model to take too large of steps and jump over the minimum. Or maybe it's too low, causing the model to get stuck in a local minimum. I've tried different learning rates. Another possible cause is that the dataset has some issues. Are the labels correct? Is the data properly formatted? I've checked and re-checked my data, but it all seems to be fine. It is an extremely frustrating situation. I hope we can find some solutions to this problem, so let's keep digging.

Experiment Details and Troubleshooting Steps

Now, let's talk about what I've tried so far. I’ve made some specific adjustments. I first started with the standard training setup, using the recommended hyperparameters. When the divergence issue popped up, my initial thought was to tweak the learning rate. I experimented with various learning rates, starting low (like 1e-5) and gradually increasing them. I even tried some adaptive learning rate methods, like AdamW, in the hope of finding a sweet spot. Unfortunately, none of these adjustments made a noticeable difference. The loss curves consistently showed that upward trend, regardless of the learning rate. Then, I turned my attention to the KL divergence term. Based on some recent research, it seemed like the KL divergence might not be essential for this specific task, and could even be a source of instability. I disabled the KL divergence term, keeping all other parameters untouched. Unfortunately, this didn't solve the issue either. This made me think that the problem might lie elsewhere. I made sure to check the usual suspects. I ensured my data pipeline was correct, verified that the Math12K dataset was correctly loaded and preprocessed, and checked to see if there were any data corruption issues. I spent a considerable amount of time inspecting my code for any potential bugs or errors. One mistake in my code could certainly lead to these kinds of problems. I have checked the input data. Perhaps the formatting was wrong or the inputs had bad values. All seemed good. The dataset is well-structured and is a standard for training and evaluating math models. I have to say I'm feeling a bit stuck, so I'm hoping that someone out there has some experience with this. Perhaps somebody has encountered and fixed a similar problem, or maybe someone can suggest a totally new avenue to pursue. Any insight would be greatly appreciated. In my experience, even the smallest detail can be the key to unlocking these types of problems.

Detailed Steps and Settings:

  • Model: Qwen 2.5 7B
  • Dataset: Math12K
  • Hardware: Specifics here would be useful, but let's assume a powerful GPU setup.
  • Software: PyTorch or your preferred deep learning framework.
  • Learning Rates: A range of values tested, from 1e-5 to 1e-3, including adaptive optimizers.
  • KL Divergence: Disabled in some experiments.
  • Batch Size: Common batch sizes (e.g., 8, 16, 32) were tested. This is important to test.
  • Optimizer: Primarily AdamW.
  • Loss Function: Standard cross-entropy, likely with some form of regularization.

I want to emphasize that I'm trying to reproduce the results in the paper. I've read and re-read the original paper, and I tried to follow the steps as closely as possible. Unfortunately, I'm just not getting the same results. Let's make sure that everything is correct. Let's see if we can find some other possible causes.

Potential Causes and Possible Solutions

Okay, so if the learning rate, KL divergence, and data pipeline aren't the primary culprits, what else could be causing this PG loss divergence? Let's brainstorm some possibilities and potential solutions, guys. First, overfitting could be the problem. If the model is overfitting, it might be memorizing the training data instead of learning generalizable patterns. This can lead to a divergence on the validation set. If this is the case, then applying some regularization techniques, such as weight decay or dropout, can help. Gradient explosion is another possibility. This occurs when the gradients become extremely large during training, leading to instability and divergence. Clipping the gradients can prevent this, keeping them within a reasonable range. Another common issue is data imbalance. If the Math12K dataset has an uneven distribution of problem types or difficulty levels, the model might struggle to learn effectively. This could lead to a divergence. Re-balancing the data or using a weighted loss function might help. In addition, the initialization of the model weights could be problematic. If the weights are initialized poorly, the model might start training in a bad state, leading to divergence. Try different initialization methods, like Xavier or Kaiming initialization. In some cases, numerical instability can cause problems. Double-check the calculations for potential overflow or underflow issues, especially when working with very large or very small numbers. Using mixed-precision training (FP16 or BF16) can also help with stability. Also, it's possible that there's a problem with the model architecture itself. If the architecture is not suitable for the Math12K dataset, it could struggle to learn effectively. Double-check whether this architecture is really the best choice for this task. It's often the last thing to check. Finally, let's not forget about the hardware and software configuration. Ensure your GPU drivers are up to date, and that your PyTorch (or TensorFlow) version is compatible with your hardware. Sometimes, a simple software update can solve all your problems. I have to say that this problem is a real pain, and I'm really hoping that we can find a solution. Let's check all the things.

Call for Help and Discussion

So, has anyone else out there experienced similar training divergence issues with the Qwen 2.5 7B model or other large language models on the Math12K dataset? If so, what steps did you take to resolve it? Any suggestions, tips, or insights would be greatly appreciated. Any and all ideas are welcome! I'm particularly interested in hearing about:

  • Specific hyperparameter settings that worked for you.
  • Any data preprocessing steps that were crucial.
  • Any tricks or techniques for stabilizing training.
  • Whether the Math12K dataset itself might be the problem.

Let's get a conversation going! Please share your experiences, thoughts, and any solutions that you've found. I'm eager to learn and get this model training successfully. I would love to hear from you. We can solve this problem together!

Thank you in advance for your help! Let's get this model working.