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Quwsar Ohi

quwsarohi
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https://quwsarohi.github.io/

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updated a model about 24 hours ago
quwsarohi/NanoAgent-135M
published a model 27 days ago
quwsarohi/NanoAgent-135M
reacted to Kseniase's post with 👍 about 2 months ago
6 Recent & free sources to master Reinforcement Learning Almost every week new research and resources on RL come out. Knowledge needs to be constantly refreshed and updated with the latest trends. So today, we’re sharing 6 free sources to help you stay on track with RL: 1. A Survey of Continual Reinforcement Learning → https://arxiv.org/abs/2506.21872 Covers continual RL (CRL): how agents can keep learning and adapt to new tasks without forgetting past ones. It analyses methods, benchmarks, evaluation metrics &challenges 2. The Deep Reinforcement Learning course by Hugging Face → https://huggingface.co/learn/deep-rl-course/unit0/introduction This is a popular free course, regularly updated. Includes community interaction, exercises, leaderboards, etc. 3. Reinforcement Learning Specialization (Coursera, University of Alberta) → https://www.coursera.org/specializations/reinforcement-learning A 4-course series introducing foundational RL, implementing different algorithms, culminating in a capstone. It's a great structured path 4. A Technical Survey of Reinforcement Learning Techniques for LLMs → https://huggingface.co/papers/2507.04136 Looks at how RL is being used for/with LLMs for alignment, reasoning, preference signals, etc. Covers methods like RLHF, RLAIF, DPO, PPO, GRPO & applications from code gen to tool use 5. A Survey of Reinforcement Learning for Software Engineering → https://arxiv.org/abs/2507.12483 Good if you're interested in RL-applied domains. Examines how RL is used in software engineering tasks: maintenance, development, evaluation. Covering 115 papers since DRL introduction, it summarizes trends, gaps & challenges 6. A Survey of Reinforcement Learning for LRMs → https://arxiv.org/abs/2509.08827 Tracks the way from LLMs to LRMs via RL. Covers reward design, policy optimization, use cases and future approaches like continual, memory, model-based RL and more If you liked this, subscribe to The Turing Post https://www.turingpost.com/subscribe
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