Move GRPO training out of default finetune pipeline

finetune/CLAUDE.md

@@ -38,11 +38,11 @@ The schema is enforced by `dataset/schema.py:TrainingExample` (Pydantic model).
 
 | Repository | Purpose |
 |------------|---------|
-| `tobil/qmd-query-expansion-1.7B` | Final merged model (SFT + GRPO) |
+| `tobil/qmd-query-expansion-1.7B` | Final merged model (SFT baseline) |
 | `tobil/qmd-query-expansion-1.7B-gguf` | GGUF quantized versions for deployment |
 | `tobil/qmd-query-expansion-1.7B-sft` | SFT adapter checkpoint (intermediate) |
-| `tobil/qmd-query-expansion-1.7B-grpo` | GRPO adapter checkpoint (intermediate) |
 | `tobil/qmd-query-expansion-train` | Prepared training dataset |
+| `tobil/qmd-query-expansion-1.7B-grpo` | Experimental GRPO adapter (optional) |
 
 **Rules:**
 - No versioned repos (`-v1`, `-v2`, `-v4`, etc.) - update in place
@@ -80,14 +80,14 @@ uv run train.py sft --config configs/sft.yaml
 hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 2h jobs/sft.py
 ```
 
-### Stage 2: GRPO
+### Stage 2: (Experimental) GRPO
 
 ```bash
-# Local (requires CUDA)
+# Local (optional; experimental)
-uv run train.py grpo --config configs/grpo.yaml
+uv run train.py grpo --config experiments/grpo/grpo.yaml
 
-# Cloud (HuggingFace Jobs)
+# Experimental script
-hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 4h jobs/grpo.py
+HF_TOKEN=${HF_TOKEN} uv run experiments/grpo/grpo.py
 ```
 
 ### HuggingFace Jobs
@@ -102,9 +102,9 @@ hf jobs cancel <job-id>       # Cancel a job
 ### Evaluation
 
 ```bash
-uv run eval.py --model ./outputs/grpo
+uv run eval.py ./outputs/sft
-uv run eval.py --model tobil/qmd-query-expansion-1.7B
+uv run eval.py tobil/qmd-query-expansion-1.7B
-uv run eval.py --model ./outputs/grpo -o eval_results.json
+uv run eval.py ./outputs/sft -o eval_results.json
 ```
 
 ## Quality Scoring
@@ -126,6 +126,9 @@ experiments/
 ├── lfm2/          # LiquidAI LFM2-1.2B (hybrid architecture, faster inference)
 │   ├── sft_lfm2.yaml
 │   └── sft_lfm2.py
+├── grpo/          # Experimental GRPO recipe and config
+│   ├── grpo.py
+│   └── grpo.yaml
 └── gepa/          # DSPy-based prompt optimization (GEPA)
     ├── dspy_gepa.py
     └── ...
@@ -138,7 +141,7 @@ These are not part of the main training pipeline.
 ```
 finetune/
 ├── reward.py          # Scoring function (single source of truth)
-├── train.py           # Unified SFT + GRPO training
+├── train.py           # SFT training entrypoint
 ├── eval.py            # Generate and score expansions
 ├── convert_gguf.py    # GGUF conversion
 ├── SCORING.md         # Detailed scoring rubric
@@ -147,8 +150,8 @@ finetune/
 ├── data/              # All training JSONL files (strict schema)
 ├── dataset/           # Schema + data tools (Pydantic-based)
 ├── jobs/              # Self-contained HuggingFace Jobs scripts
-├── configs/           # Training configs (sft.yaml, grpo.yaml)
+├── configs/           # Training configs (sft.yaml)
 ├── evals/             # Test queries
-├── experiments/       # Experimental configs (LFM2, GEPA)
+├── experiments/       # Experimental configs (LFM2, GEPA, GRPO)
 └── outputs/           # Local training outputs (gitignored)
 ```

finetune/Justfile

@@ -26,6 +26,9 @@ train-local:
     HF_TOKEN=${HF_TOKEN} uv run torchrun --standalone --nproc_per_node auto \
       train.py sft --config configs/sft_local.yaml |& tee /tmp/qmd-sft-train.log
 
-grpo-local:
+# Experimental GRPO training is in finetune/experiments/grpo and not part of
-    CUDA_VISIBLE_DEVICES=1,2,3 HF_TOKEN=${HF_TOKEN} uv run torchrun --standalone --nproc_per_node 3 \
+# the default pipeline.
-      train.py grpo --config configs/grpo.yaml |& tee /tmp/qmd-grpo-train.log
+#
+# grpo-local:
+#   HF_TOKEN=${HF_TOKEN} uv run train.py grpo --config experiments/grpo/grpo.yaml |& tee /tmp/qmd-grpo-train.log
+

finetune/README.md

@@ -40,22 +40,23 @@ These feed into QMD's three search backends:
 # 1. SFT: teach the model the output format (~45 min on A10G, ~$1.50)
 hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 2h jobs/sft.py
 
-# 2. GRPO: RL refinement on top of SFT (~20 min on A10G, ~$0.50)
+# 2. Evaluate against test queries (needs local GPU or use eval job)
-hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 4h jobs/grpo.py
+uv run eval.py tobil/qmd-query-expansion-1.7B
 
-# 3. Evaluate against test queries (needs local GPU or use eval job)
+# 3. Convert to GGUF for local deployment (Ollama, llama.cpp)
-uv run eval.py --model tobil/qmd-query-expansion-1.7B-grpo \
-               --sft-model tobil/qmd-query-expansion-1.7B-sft
-
-# 4. Convert to GGUF for local deployment (Ollama, llama.cpp)
 uv run convert_gguf.py --size 1.7B
+
+# NOTE: GRPO is currently experimental and moved to finetune/experiments/grpo
+# if you want to run it manually, use uv run python experiments/grpo/grpo.py
 ```
 
 ### Local training (if you have a GPU)
 
 ```bash
 uv run train.py sft  --config configs/sft.yaml
-uv run train.py grpo --config configs/grpo.yaml
+
+# Experimental GRPO
+uv run train.py grpo --config experiments/grpo/grpo.yaml
 ```
 
 ### Monitoring HF Jobs
@@ -85,19 +86,19 @@ direct `lex:/vec:/hyde:` output without `<think>` blocks.
 ```
 finetune/
 ├── reward.py          # Scoring/reward function (single source of truth)
-├── train.py           # Unified SFT + GRPO training (two subcommands)
+├── train.py           # SFT training entrypoint
 ├── eval.py            # Generate expansions and score them
 ├── convert_gguf.py    # GGUF conversion for Ollama/llama.cpp
 ├── jobs/
 │   ├── sft.py         # Self-contained SFT for HuggingFace Jobs
-│   ├── grpo.py        # Self-contained GRPO for HuggingFace Jobs
 │   ├── eval.py        # Self-contained eval for HuggingFace Jobs
 │   └── eval_common.py # Shared eval utilities
 ├── configs/
-│   ├── sft.yaml       # SFT hyperparameters for Qwen3-1.7B
+│   └── sft.yaml       # SFT hyperparameters for Qwen3-1.7B
-│   └── grpo.yaml      # GRPO hyperparameters for Qwen3-1.7B
 ├── evals/
 │   └── queries.txt    # 31 test queries across 8 categories
+├── experiments/
+│   └── grpo/          # Experimental GRPO configuration and script (optional)
 ├── data/              # Training JSONL files (all concatenated for training)
 ├── dataset/
 │   ├── prepare_data.py     # Format for Qwen3 chat template, dedup, split
@@ -130,29 +131,14 @@ uv run train.py sft --config configs/sft.yaml
 uv run train.py sft --config configs/sft.yaml --dry-run  # preview config
 ```
 
-### Stage 2: GRPO (Group Relative Policy Optimization)
+### Stage 2: (Experimental) GRPO
 
-Reinforcement learning on top of the merged SFT weights. The model generates
+GRPO is currently treated as experimental and kept under `experiments/grpo/`.
-multiple expansions per query, they are scored by the reward function, and the
+It is not part of the default production path for this repository.
-model is updated to prefer higher-scoring outputs.
-
-| Parameter | Value |
-|-----------|-------|
-| Base | Merged SFT checkpoint |
-| Method | LoRA (rank 4, alpha 8) — smaller for RL stability |
-| Target modules | q_proj, v_proj only |
-| Reward | `reward.py` (rule-based, 5 dimensions) |
-| KL beta | 0.04 — prevents drift from SFT checkpoint |
-| Generations per prompt | 4 |
-| Max steps | 200 |
-| Learning rate | 5e-7 |
-
-**Important:** `beta > 0` is critical. With `beta=0` the model experiences
-catastrophic drift and scores drop to 0%.
 
 ```bash
-uv run train.py grpo --config configs/grpo.yaml
+# Optional experimental GRPO run
-uv run train.py grpo --config configs/grpo.yaml --dry-run  # test reward function
+uv run train.py grpo --config experiments/grpo/grpo.yaml
 ```
 
 ## Evaluation
@@ -160,24 +146,26 @@ uv run train.py grpo --config configs/grpo.yaml --dry-run  # test reward functio
 `eval.py` generates expansions from a model and scores them against test queries:
 
 ```bash
-# Evaluate an SFT model
+# Evaluate a SFT model
 uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft
 
-# Evaluate a GRPO model (needs SFT adapter merged first)
+# Evaluate an SFT output dir
-uv run eval.py --model tobil/qmd-query-expansion-1.7B-grpo \
+uv run eval.py outputs/sft
-               --sft-model tobil/qmd-query-expansion-1.7B-sft
 
 # Verbose output with deduction details
-uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft -v
+uv run eval.py tobil/qmd-query-expansion-1.7B -v
+
+# Optional: evaluate GRPO experimental output (if run)
+uv run eval.py outputs/grpo
 
 # Save detailed scores to JSON
-uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft -o scores.json
+uv run eval.py tobil/qmd-query-expansion-1.7B -o scores.json
 ```
 
 ## Reward Function
 
-`reward.py` is the single source of truth for scoring. It is used both as the
+`reward.py` is the single source of truth for scoring. It is used for evaluation
-GRPO reward signal during training and for evaluation.
+and (optionally) as the GRPO reward signal in the experimental path.
 
 Five scoring dimensions (max 120 without hyde, 140 with):
 
@@ -201,8 +189,8 @@ uv run reward.py
 
 ## GGUF Conversion
 
-Merges base + SFT + GRPO adapters into a single model and produces
+Merges base + SFT and (optionally) GRPO adapters into a single model, then
-quantized GGUF files for deployment:
+produces quantized GGUF files for deployment:
 
 ```bash
 # Use preset for 1.7B
@@ -240,15 +228,14 @@ just validate
 
 ## Architecture Notes
 
-The two-stage training approach (SFT -> GRPO) is standard for structured-output models:
+The production training approach is currently **SFT-only**:
 
 1. **SFT** establishes format compliance and basic query understanding. It uses
    a large LoRA (rank 16, all projection layers) because it needs to learn a
    new output format from scratch.
 
-2. **GRPO** refines quality within the learned format. It uses a small LoRA
+2. **GRPO** exists as an optional experimental path under `experiments/grpo/`
-   (rank 4, q/v only) and KL regularization to make incremental improvements
+   and is not in the production training pipeline.
-   without losing what SFT taught.
 
 The reward function is entirely rule-based (no LLM judge) which makes it fast,
 deterministic, and suitable as an RL signal. See `SCORING.md` for the full rubric.
@@ -266,20 +253,12 @@ deterministic, and suitable as an RL signal. See `SCORING.md` for the full rubri
 | Epochs | 5 |
 | Hardware | A10G (24 GB VRAM) |
 
-### GRPO
-
-| Metric | Value |
-|--------|-------|
-| Mean reward | 0.757 |
-| Final loss | 0.0005 |
-| KL divergence | 0.00048 |
-| Mean completion length | ~58 tokens |
-| Training time | ~19 min (200 steps) |
-| Hardware | A10G (24 GB VRAM) |
-
 ### Evaluation Scores
 
 | Model | Average Score | Excellent (30) |
 |-------|--------------|-----------------|
 | SFT | 92.0% | 30/30 |
-| GRPO | 91.7% | 30/30 |
+
+> GRPO scores are not tracked in this branch; see `experiments/grpo/` for historical
+> experimental results.
+

finetune/experiments/grpo/README.md

@@ -0,0 +1,26 @@
+# GRPO (Experimental)
+
+This folder contains the **experimental** GRPO training path for query expansion.
+It is not part of the default production pipeline.
+
+## Files
+
+- `grpo.yaml` – experimental GRPO hyperparameters
+- `grpo.py` – standalone GRPO training script
+
+## Run
+
+```bash
+# Recommended default: run from repo root
+cd /home/tobi/qmd
+uv run finetune/experiments/grpo/grpo.py
+
+# Or use unified entrypoint (deprecated in main pipeline):
+uv run train.py grpo --config finetune/experiments/grpo/grpo.yaml
+```
+
+## Notes
+
+- Current mainline focuses on SFT-only quality and benchmarks.
+- Keep this workflow isolated unless you are explicitly experimenting with
+  reinforcement-learning refinement.

finetune/experiments/grpo/grpo.py

@@ -14,8 +14,10 @@
 """
 GRPO training for QMD query expansion (Qwen3-1.7B).
 
-Runs on top of merged SFT weights. Self-contained for HuggingFace Jobs:
+Experimental recipe run on top of merged SFT weights. Self-contained runner:
-    hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 4h jobs/grpo.py
+    uv run experiments/grpo/grpo.py
+
+(If using HF Jobs, run this script as the job entrypoint.)
 """
 
 import os
@@ -42,7 +44,7 @@ if not os.path.exists(_eval_common_path):
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 from eval_common import QMDRewardFunction, run_eval
 
-# --- Config (inlined from configs/grpo.yaml) ---
+# --- Config (inlined from experiments/grpo/grpo.yaml) ---
 BASE_MODEL = "Qwen/Qwen3-1.7B"
 SFT_MODEL = "tobil/qmd-query-expansion-1.7B-sft"
 OUTPUT_MODEL = "tobil/qmd-query-expansion-1.7B-grpo"

finetune/experiments/grpo/grpo.yaml

@@ -1,7 +1,7 @@
 # GRPO Training Config for QMD Query Expansion
 # Target: Qwen3-1.7B, trained on top of merged SFT weights
 #
-# Usage: uv run train.py grpo --config configs/grpo.yaml
+# Usage: uv run train.py grpo --config experiments/grpo/grpo.yaml
 #
 # The reward function (reward.py) scores expansions on format compliance,
 # diversity, hyde quality, content quality, and named entity preservation.

finetune/train.py

@@ -18,14 +18,14 @@
 """
 Unified training script for QMD query expansion models.
 
-Supports two stages:
+Primary pipeline is SFT-only:
   sft  - Supervised fine-tuning on labeled examples
-  grpo - Group Relative Policy Optimization (RL) on top of merged SFT weights
+
+GRPO was moved to `experiments/grpo/` and is not part of the main training
+pipeline by default.
 
 Usage:
     uv run train.py sft  --config configs/sft.yaml
-    uv run train.py grpo --config configs/grpo.yaml
-    uv run train.py grpo --config configs/grpo.yaml --dry-run
 """
 
 import argparse
@@ -412,6 +412,15 @@ def cmd_sft(args):
 
 def cmd_grpo(args):
     """Run GRPO reinforcement learning on top of merged SFT weights."""
+    print(
+        "GRPO is not part of the main training pipeline and has been moved to `experiments/grpo/`."
+    )
+    print("To run experimental GRPO, use:")
+    print("  cd finetune && uv run python experiments/grpo/grpo.py")
+    print("Or, if you have local config wiring ready:")
+    print("  uv run train.py grpo --config experiments/grpo/grpo.yaml")
+    return
+
     import torch
     import torch.distributed as dist
     import os
@@ -645,8 +654,6 @@ def main():
         epilog="""
 Examples:
   uv run train.py sft  --config configs/sft.yaml
-  uv run train.py grpo --config configs/grpo.yaml
-  uv run train.py grpo --config configs/grpo.yaml --dry-run
         """,
     )
     sub = parser.add_subparsers(dest="stage", required=True)
@@ -657,7 +664,10 @@ Examples:
         "--dry-run", action="store_true", help="Print config and exit"
     )
 
-    grpo_parser = sub.add_parser("grpo", help="GRPO reinforcement learning")
+    grpo_parser = sub.add_parser(
+        "grpo",
+        help="Experimental: GRPO reinforcement learning (moved to experiments/grpo/)",
+    )
     grpo_parser.add_argument("--config", required=True, help="Path to GRPO config YAML")
     grpo_parser.add_argument(
         "--dry-run", action="store_true", help="Print config, test reward, and exit"