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Qwen/Qwen3-0.6B
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11 Alignment and Optimization Algorithms for LLMs When we need to align models' behavior with the desired objectives, we rely on specialized algorithms that support helpfulness, accuracy, reasoning, safety, and alignment with user preferences. Much of a model’s usefulness comes from post-training optimization methods. Here are the main optimization algorithms (both classic and new) in one place: 1. PPO (Proximal Policy Optimization) -> https://huggingface.co/papers/1707.06347 Clips the probability ratio to prevent the new policy from diverging too far from the old one. It helps keep everything stable 2. DPO (Direct Preference Optimization) -> https://huggingface.co/papers/2305.18290 It's a non RL method, where an LM is an implicit reward model. It uses a simple loss to boost the preferred answer’s probability over the less preferred one 3. GRPO (Group Relative Policy Optimization) -> https://huggingface.co/papers/2402.03300 An RL method that compares a group of model outputs for the same input and updates the policy based on relative rankings. It doesn't need a separate critic model It's latest application is Flow-GRPO which adds online RL into flow matching models -> https://huggingface.co/papers/2505.05470 4. DAPO (Decoupled Clip and Dynamic sAmpling Policy Optimization) -> https://huggingface.co/papers/2503.14476 Decouples the clipping bounds for flexibility, introducing 4 key techniques: clip-higher (to maintain exploration), dynamic sampling (to ensure gradient updates), token-level loss (to balance learning across long outputs), and overlong reward shaping (to handle long, truncated answers) 5. Supervised Fine-Tuning (SFT) -> https://huggingface.co/papers/2203.02155 Often the first post-pretraining step. A model is fine-tuned on a dataset of high-quality human-written input-output pairs to directly teach desired behaviors More in the comments 👇 If you liked it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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ACE-Step/ACE-Step-v1-3.5B
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