SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs

Why Multi-Task Learning Matters for LLMs

When training a Large Language Model (LLM) to handle multiple tasks — translation, summarization, question answering, coding — we face a fundamental paradox: excelling at one task often degrades performance on another. This phenomenon, known as negative transfer, is the biggest obstacle to building truly versatile LLMs.

The paper “SFT Conflicts, RL Coexists,” published today (August 11, 2026) on HuggingFace Papers, provides a deep theoretical and empirical analysis of this phenomenon, comparing the two dominant training paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

SFT Creates Conflicts, RL Enables Coexistence

The research reveals a fundamental difference between the two approaches:

SFT (Supervised Fine-Tuning): When fine-tuning a model on multiple tasks simultaneously using labeled data, tasks compete for weight updates. Gradients from task A can cancel gradients from task B, preventing good convergence on either. This is the unavoidable “conflict” in multi-task SFT.

RL (Reinforcement Learning): In contrast, when using reinforcement learning with separate reward models for each task, tasks can “coexist” without direct conflict. RL allows the model to learn different policies for different tasks, and their interaction becomes more cooperative than competitive.

Practical Implications for Enterprises

For businesses building internal LLMs, this research carries three important implications:

1. Prioritize RL for multi-task scenarios. If you need a model that handles translation, summarization, and question answering simultaneously, RL is the better choice over SFT. While more computationally expensive, the final results are more stable.

2. SFT remains useful for single-task. When fine-tuning for one specific task (e.g., a customer support chatbot), SFT is still the simplest and most effective approach. Conflicts only arise with multiple tasks.

3. Combine both approaches. The optimal strategy may be: use RL to train a multi-task foundation, then apply SFT for task-specific refinement when high precision is needed.

Conclusion

This paper provides solid theoretical grounding for a phenomenon many practitioners have observed empirically: SFT is not a silver bullet for multi-task learning. RL, while more complex, delivers the flexibility and stability needed for training versatile LLMs.

For Fujigo, this research reinforces our approach to AI solution development: rather than forcing one model to do everything via SFT, we need smarter training architectures that combine RL and SFT strategically.

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Source: HuggingFace Papers — #1 trending paper on August 11, 2026 with 32 upvotes.