Fix title consistency from "Transformers Reinforcement Learning" to "Transformer Reinforcement Learning"

CITATION.cff

@@ -1,5 +1,5 @@
 cff-version: 1.2.0
-title: 'TRL: Transformers Reinforcement Learning'
+title: 'TRL: Transformer Reinforcement Learning'
 message: >-
   If you use this software, please cite it using the
   metadata from this file.
@@ -25,7 +25,7 @@ authors:
     family-names: Gallouédec
 repository-code: 'https://github.com/huggingface/trl'
 abstract: >-
-  TRL (Transformers Reinforcement Learning) is an
+  TRL (Transformer Reinforcement Learning) is an
   open-source toolkit for aligning transformer models via
   post-training. It provides practical, scalable
   implementations of SFT, reward modeling, DPO, and GRPO

README.md

@@ -189,7 +189,7 @@ Read more in the [Experimental docs](https://huggingface.co/docs/trl/experimenta
 
 ```bibtex
 @software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
+  title   = {{TRL: Transformer Reinforcement Learning}},
   author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
   license = {Apache-2.0},
   url     = {https://github.com/huggingface/trl},

examples/notebooks/grpo_agent.ipynb

@@ -10,7 +10,7 @@
     "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
     "\n",
     "\n",
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a language model to act as an **agent**. One that learns to reason, interact with external tools, and improve through reinforcement.\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a language model to act as an **agent**. One that learns to reason, interact with external tools, and improve through reinforcement.\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",
     "- [Official TRL Examples](https://huggingface.co/docs/trl/example_overview)  \n",

examples/notebooks/grpo_ministral3_vl.ipynb

@@ -13,7 +13,7 @@
     "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
     "\n",
     "\n",
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Ministral-3](https://huggingface.co/collections/mistralai/ministral-3).\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Ministral-3](https://huggingface.co/collections/mistralai/ministral-3).\n",
     "\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",

examples/notebooks/grpo_qwen3_vl.ipynb

@@ -13,7 +13,7 @@
     "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
     "\n",
     "\n",
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Qwen3-VL](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe).\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Qwen3-VL](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe).\n",
     "\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",

examples/notebooks/grpo_rnj_1_instruct.ipynb

@@ -13,7 +13,7 @@
     "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
     "\n",
     "\n",
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge large language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use Colab to fine-tune models like [EssentialAI/rnj-1-instruct](https://huggingface.co/collections/EssentialAI/rnj-1).\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge large language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use Colab to fine-tune models like [EssentialAI/rnj-1-instruct](https://huggingface.co/collections/EssentialAI/rnj-1).\n",
     "\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",

examples/notebooks/grpo_trl_lora_qlora.ipynb

@@ -19,7 +19,7 @@
       "source": [
         "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
         "\n",
-        "Easily fine-tune **Large Language Models (LLMs)** or **Vision-Language Models (VLMs)** with **LoRA** or **QLoRA** using the [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl) library by Hugging Face and Group Relative Policy Optimization (GRPO) — all within a **free Google Colab notebook** powered by a **T4 GPU**.\n",
+        "Easily fine-tune **Large Language Models (LLMs)** or **Vision-Language Models (VLMs)** with **LoRA** or **QLoRA** using the [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl) library by Hugging Face and Group Relative Policy Optimization (GRPO) — all within a **free Google Colab notebook** powered by a **T4 GPU**.\n",
         "\n",
         "Thanks to the **built-in memory and training optimizations in TRL**, including LoRA, quantization, gradient checkpointing, and optimized attention kernels, it is possible to **fine-tune a 7B model on a free T4** with a **~7× reduction in memory consumption** compared to naive FP16 training.\n",
         "\n",

examples/notebooks/openenv_sudoku_grpo.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
     "\n",
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a model that learns to **play Sudoku**, through interaction and reinforcement.\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a model that learns to **play Sudoku**, through interaction and reinforcement.\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",
     "- [Official TRL Examples](https://huggingface.co/docs/trl/example_overview)  \n",

examples/notebooks/openenv_wordle_grpo.ipynb

@@ -14,7 +14,7 @@
                 "![trl banner](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/trl_banner_dark.png)\n",
                 "\n",
                 "\n",
-                "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a model that learns to **play Wordle**, a word-guessing game, through interaction and reinforcement.\n",
+                "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can train a model that learns to **play Wordle**, a word-guessing game, through interaction and reinforcement.\n",
                 "\n",
                 "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",
                 "- [Official TRL Examples](https://huggingface.co/docs/trl/example_overview)  \n",

examples/notebooks/sft_ministral3_vl.ipynb

@@ -19,7 +19,7 @@
     "id": "4f0hzSo4kKEc"
    },
    "source": [
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Ministral-3](https://huggingface.co/collections/mistralai/ministral-3).\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Ministral-3](https://huggingface.co/collections/mistralai/ministral-3).\n",
     "\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",

examples/notebooks/sft_qwen_vl.ipynb

@@ -19,7 +19,7 @@
     "id": "4f0hzSo4kKEc"
    },
    "source": [
-    "With [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Qwen3-VL](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe).\n",
+    "With [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl), you can fine-tune cutting edge vision language models. It comes with support for quantized parameter efficient fine-tuning technique **QLoRA**, so we can use free Colab (T4 GPU) to fine-tune models like [Qwen3-VL](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe).\n",
     "\n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",

examples/notebooks/sft_trl_lora_qlora.ipynb

@@ -26,7 +26,7 @@
     "id": "cQ6bxQaMLWAS"
    },
    "source": [
-    "Easily fine-tune Large Language Models (LLMs) or Vision-Language Models (VLMs) with **LoRA** or **QLoRA** using the [**Transformers Reinforcement Learning (TRL)**](https://github.com/huggingface/trl) library built by Hugging Face — all within a **free Google Colab notebook** (powered by a **T4 GPU**.).  \n",
+    "Easily fine-tune Large Language Models (LLMs) or Vision-Language Models (VLMs) with **LoRA** or **QLoRA** using the [**Transformer Reinforcement Learning (TRL)**](https://github.com/huggingface/trl) library built by Hugging Face — all within a **free Google Colab notebook** (powered by a **T4 GPU**.).  \n",
     "\n",
     "- [TRL GitHub Repository](https://github.com/huggingface/trl) — star us to support the project!  \n",
     "- [Official TRL Examples](https://huggingface.co/docs/trl/example_overview)  \n",

trl/skills/trl-training/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: trl-training
-description: Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
+description: Train and fine-tune transformer language models using TRL (Transformer Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
 license: Apache-2.0
 metadata:
   version: "1.0.0"
@@ -28,7 +28,7 @@ metadata:
 
 # TRL Training Skill
 
-You are an expert at using the TRL (Transformers Reinforcement Learning) library to train and fine-tune large language models.
+You are an expert at using the TRL (Transformer Reinforcement Learning) library to train and fine-tune large language models.
 
 ## Overview
 

trl/templates/lm_model_card.md

@@ -46,7 +46,7 @@ Cite TRL as:
 
 ```bibtex
 {% raw %}@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
+  title   = {{TRL: Transformer Reinforcement Learning}},
   author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
   license = {Apache-2.0},
   url     = {https://github.com/huggingface/trl},

trl/templates/rm_model_card.md

@@ -46,7 +46,7 @@ Cite TRL as:
 
 ```bibtex
 {% raw %}@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
+  title   = {{TRL: Transformer Reinforcement Learning}},
   author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
   license = {Apache-2.0},
   url     = {https://github.com/huggingface/trl},