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rotem israeli

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https://rotem154154.github.io

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liked a model about 2 hours ago
trillionlabs/Tri-7B
reacted to Kseniase's post with 👍 about 5 hours ago
9 new policy optimization techniques Reinforcement Learning (RL) won't stuck in the same old PPO loop - in the last two months alone, researchers have introduced a new wave of techniques, reshaping how we train and fine-tune LLMs, VLMs, and agents. Here are 9 fresh policy optimization techniques worth knowing: 1. GSPO: Group Sequence Policy Optimization → https://huggingface.co/papers/2507.18071 Shifts from token-level to sequence-level optimization, clipping, and rewarding to capture the full picture and increase stability compared to GRPO. GSPO-token variation also allows token-level fine-tuning. 2. LAPO: Length-Adaptive Policy Optimization → https://huggingface.co/papers/2507.15758 A two-stage RL framework that trains models to adaptively control reasoning length by learning typical solution lengths for shorter and more efficient reasoning. 3. HBPO: Hierarchical Budget Policy Optimization → https://huggingface.co/papers/2507.15844 This one trains model to adapt reasoning depth based on problem complexity. It divides training samples into subgroups with different token budgets, using budget-aware rewards to align reasoning effort with task difficulty. 4. SOPHIA: Semi-off-policy reinforcement learning → https://huggingface.co/papers/2507.16814 Combines on-policy visual understanding from the Vision Language Models (VLMs) with off-policy reasoning from an LM, assigning outcome-based rewards and propagating visual rewards backward through the reasoning steps. 5. RePO: Replay-Enhanced Policy Optimization → https://huggingface.co/papers/2506.09340 Introduces a replay buffer into on-policy RL for LLMs, retrieving diverse off-policy samples for each prompt to broaden the training data per prompt Read further below ⬇️ If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
upvoted a paper about 10 hours ago
Group Sequence Policy Optimization
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