FlowRL: Matching Reward Distributions for LLM Reasoning
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2509.15207
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Kaiyan Zhang
iseesaw
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Large Reasoning Models, Reinforcement Learning, Agent
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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 → A Technical Survey of Reinforcement Learning Techniques for Large Language Models (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
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 → A Technical Survey of Reinforcement Learning Techniques for Large Language Models (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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thanks for sharing our work
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fascinating read!
staying bullish on search with rl might just help us get rid of hallucination entirely. I really like their approach:
1) <think>on prompt/context && what u know </think>
2) self <search>when u don’t know</search> (iteratively) with no external tool
3) <information>cite sources to support claim(s)</information>
4) <answer>final answer</answer>
their rl training was done cost efficiently too, see code: https://github.com/TsinghuaC3I/SSRL
staying bullish on search with rl might just help us get rid of hallucination entirely. I really like their approach:
1) <think>on prompt/context && what u know </think>
2) self <search>when u don’t know</search> (iteratively) with no external tool
3) <information>cite sources to support claim(s)</information>
4) <answer>final answer</answer>
their rl training was done cost efficiently too, see code: https://github.com/TsinghuaC3I/SSRL
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