From 2f5221840fcd0ef3dfb73ab7067f2ce78e5a2b65 Mon Sep 17 00:00:00 2001 From: aoshen02 Date: Mon, 29 Jun 2026 02:04:44 +0000 Subject: [PATCH] docs: fix dead examples/README link to low_precision MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit examples/README.md links low_precision to ./low_precision, but that directory no longer exists: #1432 moved the low-precision example out of examples/ — the run scripts went to scripts/low_precision/ and the writeup to docs/{en,zh}/advanced/low-precision.md. Repoint the README link to ../scripts/low_precision so it resolves again. Co-Authored-By: Claude Opus 4.8 (1M context) Signed-off-by: aoshen02 --- examples/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 128b1562d4..9ed2afa438 100644 --- a/examples/README.md +++ b/examples/README.md @@ -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.