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  "notes": "The chart shows that LoRA ranks 1, 16, and 256 perform similarly to full fine-tuning in terms of peak accuracy, but with different stability profiles.",
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      "text": "Thinking Machines show that RL can match full fine-tuning even with rank-1 Low-Rank Adaptation (LoRA).",
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      "text": "With LoRA you insert tiny adapters in a few attention and MLP layers and update only those during PPO, GRPO, or RLHF. The backbone does not change.\nThis cuts the trainable parameters from billions to millions, so gradients and optimizer state shrink by roughly 10–50x. Memory pressure falls even further when you pair LoRA with 8-bit weights.\nUnder the same budget you can move from a 7-13B class model to a much larger 30-70B class model. You can also fit longer contexts or larger batches on the same cards.\nVery low adapter ranks can, however, underfit. Reasonable choices are ranks in the 16–64 range and placing adapters in the layers that matter for the skill you want to improve.",
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      "text": "Thinking Machines show that RL can match full fine-tuning even with rank-1 Low-Rank Adaptation (LoRA). In policy-gradient setups, LoRA updates only tiny adapters while the backbone stays frozen, yet it reaches the same peak performance, often with a wider range of stable learning rates. The reason is that RL supplies very few bits per episode, so even tiny adapters have ample capacity to absorb what RL can teach.",
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      "text": "Bigger models, same budget: RL with LoRA adapters",
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