Revert hiyouga#29 and hiyouga#30 (hiyouga#32)

Revert "Add Axolotl Training Blueprint with Full Fine-tuning
Example(hiyouga#29)" and Revert "Add EasyR1 Reinforcement Learning Experiment
(hiyouga#30)"

Refs: NOTICKET

Author: gcheron

README-e2e.md

@@ -32,12 +32,6 @@ Using this method, the test will run on `staging` or `production` depending on w
 
 To execute a test directly a simple trampoline is provided, for example:
 
-**LlamaFactory test:**
 ```bash
 ./ci/e2e_tests/gpt2-simple.py
 ```
-
-**Axolotl test:**
-```bash
-./ci/e2e_tests/axolotl-simple.py
-```

README.md

@@ -24,11 +24,9 @@ The following table lists out the experiments available in this repository. Each
 | 8   | [Fine-Tuning a Text-to-Speech Model](/experiments/ft-on-a-tts-model/README.md)                                                   | Fine-tune a text-to-speech (TTS) model                                                                                     |
 | 9   | [Fine-Tuning a language Model using _Flash Attention_](/experiments/flash-attention-ft-on-a-language-model/README.md)            | Fine-tune a causal language model efficiently using the [flash-attn package](https://github.com/Dao-AILab/flash-attention) |
 | 10  | [Fine-Tuning a language Model with LlamaFactory](/experiments/llama-factory/README.md)                                           | Demonstrates how to use [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) with FlexAI                               |
-| 11  | [Fine-Tuning a language Model with Axolotl](/experiments/axolotl/README.md)                                                      | Demonstrates how to use [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) with FlexAI                         |
-| 12  | [Reinforcement Learning Fine-Tuning with EasyR1](/experiments/easyR1/README.md)                                                  | Train language models with RL using GRPO and DAPO algorithms for enhanced reasoning capabilities                           |
-| 13  | [RAG Application with LangChain and FlexAI Inference Endpoints](/experiments/rag-application/README.md)                          | Interactive interface for users to ask questions based on provided documents using Retrieval-Augmented Generation         |
-| 14  | [Speech-to-Text Application Using FlexAI Inference Endpoints](/experiments/speech-to-text-inference/README.md)                   | Interactive interface for recording audio messages and receiving transcriptions                                            |
-| 15  | [Multi-Agent Orchestration with LangGraph](/experiments/multi-agent/README.md)                                                   | Build a multi-agent system where specialized AI agents work together under a central supervisor                            |
+| 11  | [RAG Application with LangChain and FlexAI Inference Endpoints](/experiments/rag-application/README.md)                                                                        | Interactive interface for users to ask questions based on provided documents using Retrieval-Augmented Generation         |
+| 12  | [Speech-to-Text Application Using FlexAI Inference Endpoints](/experiments/speech-to-text-inference/README.md)                                                    | Interactive interface for recording audio messages and receiving transcriptions                                     |
+| 13  | [Multi-Agent Orchestration with LangGraph](/experiments/multi-agent/README.md)                                                   | Build a multi-agent system where specialized AI agents work together under a central supervisor                            |
 
 ---