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docs: fix dead examples/README link to low_precision #2142

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docs: fix dead examples/README link to low_precision #2142

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2 changes: 1 addition & 1 deletion examples/README.md
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Expand Up @@ -8,7 +8,7 @@ These examples provide concrete examples to leverage slime in your own RL workfl
- **[fully_async](./fully_async)**: Demonstrates fully asynchronous rollout generation for higher efficiency.
- **[geo3k_vlm](./geo3k_vlm)**: Training VLMs on a single-turn reasoning task using GRPO on the GEO3K dataset.
- **[geo3k_vlm_multi_turn](./geo3k_vlm_multi_turn)**: VLM multi-turn training on Geo3k dataset.
- **[low_precision](./low_precision)**: Examples of FP8 training and inference for improved throughput and stability.
- **[low_precision](../scripts/low_precision)**: Examples of FP8 training and inference for improved throughput and stability.
- **[multi_agent](./multi_agent)**: Example of running multi-agent RL with `slime`.
- **[on_policy_distillation](./on_policy_distillation)**: Example implementation for on-policy distillation, extending the reinforcement learning pipeline to support teacher–student distillation directly within on-policy training.
- **[delta_weight_sync](./delta_weight_sync)**: Non-colocated weight sync that ships only changed positions + values over disk (training/inference disaggregation) or NCCL.
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