Trl Fine Tuning — TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF
Trl Fine Tuning — TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF
Start with meaning, then move to detail.
This lesson explains Trl Fine Tuning — TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF as part of extending Hermes and connecting external tools. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, Quick start, then verify failure modes and version compatibility.
Complete installation and one successful task before adding new capabilities.
A clear outcome before you read.
- Understand Trl Fine Tuning — TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Session & memory
- A session holds conversation context, while memory keeps selected facts that should persist.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03Quick start
Read this after the foundation, then connect it to the previous step.
- 04Common workflows
Read this after the foundation, then connect it to the previous step.
- 05Workflow 1: Full RLHF pipeline (SFT → Reward Model → RLOO)
Read this after the foundation, then connect it to the previous step.
- 06Workflow 2: Simple preference alignment with DPO
Read this after the foundation, then connect it to the previous step.
- 07Workflow 3: Memory-efficient online RL with GRPO
Read this after the foundation, then connect it to the previous step.
- 08When to use vs alternatives
Read this after the foundation, then connect it to the previous step.
- 09Common issues
Read this after the foundation, then connect it to the previous step.
- 10Advanced topics
Finish here to verify the result and special cases.
Copy only after you understand the effect.
pip install trl transformers datasets peft accelerate**DPO** (align with preferences):## Common workflows
### Workflow 1: Full RLHF pipeline (SFT → Reward Model → RLOO)
Complete pipeline from base model to human-aligned model.
> **Note (TRL 1.x):** PPO has been **removed** from TRL — `PPOTrainer`, `PPOConfig`, and
> `python -m trl.scripts.ppo` no longer exist. Use an online-RL trainer TRL still ships:
> **RLOO** (`RLOOTrainer` / `trl rloo`) is the closest drop-in for a reward-model-driven
> RLHF pipeline, and **GRPO** (`GRPOTrainer` / `trl grpo`, see Workflow 3) is the
> memory-efficient alternative. The step below uses RLOO.
Copy this checklist:Read the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.