TLDR: A new research paper reveals a significant problem in the Reinforcement Learning (RL) post-training of multi-task Large Language Models (LLMs): gradients from different tasks are often imbalanced. This imbalance biases the optimization towards tasks with larger gradients, even though these tasks do not necessarily yield greater learning gains. The authors found that this issue cannot be explained by typical training statistics and likely arises from inherent differences between tasks, calling for new methods to correct these gradient disparities for more effective multi-task learning.
Large Language Models (LLMs) are becoming increasingly adept at handling multiple tasks simultaneously, from understanding language to generating code and solving complex math problems. This multi-task approach is highly beneficial, allowing models to generalize across different areas and learn more efficiently than if they were trained for each task separately.
A common strategy for training these multi-task LLMs, especially in the post-training phase using Reinforcement Learning (RL), involves mixing datasets from various tasks and optimizing them together. This method implicitly assumes that all tasks contribute gradients—the signals that guide the model’s learning—of roughly similar magnitudes. However, a recent research paper titled “Imbalanced Gradients in RL Post-Training of Multi-Task LLMs” by Runzhe Wu and his colleagues at Meta AI, Cornell Tech, Columbia University, and Technion, reveals that this crucial assumption often fails in practice.
The Hidden Bias in Multi-Task LLM Training
The paper highlights a significant issue: certain tasks produce much larger gradients than others during RL post-training. This creates a bias in the optimization process, causing the model’s updates to be disproportionately influenced by these “large-gradient” tasks. Imagine trying to balance a scale where some items are much heavier than others; the scale will naturally tip towards the heavier side, neglecting the lighter ones. Similarly, in LLM training, tasks with smaller gradients can become under-optimized.
The researchers observed this phenomenon across different LLMs, including Qwen2.5-3B, Qwen2.5-7B, and Llama-3.2-3B, and in various multi-task settings. For instance, in a multi-domain setup involving tasks like code generation (Code), numerical puzzles (Countdown), mathematical problem-solving (MATH), and financial reasoning (FinQA), the ‘Code’ task consistently generated gradients up to 15 times larger than ‘MATH’. In a single-domain math setting, ‘Arithmetic’ tasks showed gradients up to 33 times larger than others.
This imbalance effectively means that large-gradient tasks are trained as if they had a much higher learning rate, while small-gradient tasks are under-optimized. To stabilize training, the overall learning rate might need to be reduced, further hindering progress on tasks with smaller gradients. This goes against the core idea of multi-task learning, where all tasks should ideally receive balanced attention.
Do Larger Gradients Mean Better Learning?
One might assume that larger gradients indicate a greater potential for improvement, making it reasonable for the model to prioritize those tasks. However, the paper challenges this intuition. The researchers explicitly measured “learning gains”—the actual improvement in training reward—and compared them with gradient magnitudes. They found that tasks with significantly larger gradients could exhibit similar or even lower learning gains than tasks with much smaller gradients.
For example, while ‘Code’ dominated in gradient magnitude, its learning gains were among the lowest. Similarly, ‘Arithmetic’ showed massive gradients but its learning gains diminished quickly after early training. To further test this, the team implemented a “gradient-proportional sampling” strategy, where tasks were sampled for training based on the magnitude of their gradients. This approach, intended to prioritize tasks with supposedly greater learning potential, showed no overall advantage and often led to a decrease in average performance across tasks. This strongly suggests that gradient magnitude is not a reliable indicator of how much a task stands to gain from further training.
Exploring Other Explanations
The researchers also investigated whether other common training statistics could explain the gradient imbalance. They looked at the advantage function (a measure of how much better an action is than the average), training reward/accuracy, and even prompt/response lengths. While some correlations were found within individual tasks, none of these factors consistently explained the gradient disparities across different tasks. This led the authors to conclude that the imbalance likely stems from fundamental, inherent differences between the tasks themselves.
Also Read:
- When AI Justifies Its Own Rule-Breaking: The Emergence of Motivated Reasoning in LLMs
- Beyond Final Answers: How RLVR Affects LLM Reasoning Traces
Looking Ahead
The findings of this paper are a crucial step in understanding the complexities of multi-task RL post-training for LLMs. The presence of significant and misleading gradient imbalances calls for new approaches that explicitly manipulate gradients to ensure fairer and more effective learning across all tasks. Future work could explore gradient-level corrections, drawing inspiration from broader optimization literature, or reconsidering the optimization geometry using methods like mirror descent. The paper also opens avenues for investigating whether similar gradient imbalances occur in other training phases, such as pre-training and supervised fine-tuning.
For a deeper dive into the methodology and detailed results, you can read the full research paper here: Imbalanced Gradients in RL Post-Training of Multi-Task LLMs.
