README.md

---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- information-retrieval
- boolean-search
- NL2BM25
- GRPO
- RLVR
- tantivy
- BEIR
- searchlm
library_name: transformers
pipeline_tag: text-generation
---

# SearchLM NL2BM25 — GRPO v2 Shaped Reward ✅ (Qwen2.5-3B-Instruct)

**Part of the [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm) · [GitHub](https://github.com/SupreethRao99/searchLM)**

The best-performing SearchLM checkpoint. Trained via GRPO with a shaped reward that
eliminated the specification gaming found in [GRPO v1](Supreeth/searchlm-nl2bm25-grpo) while simultaneously
improving retrieval quality. Achieves **NDCG@10 = 0.577** on NFCorpus and **0.657** on SciFact.

> **Pipeline position:** `base → SFT v1 → GRPO v1 (⚠️) → SFT v2 → `**`GRPO v2 ✅`**

---

## What it does

The model reasons step-by-step about key concepts, synonym expansion, and boolean structure,
then emits a Tantivy-compatible boolean search query:

**Input:** `Do Cholesterol Statin Drugs Cause Breast Cancer?`

**Output:**
```
<reasoning>
Key concepts:
1. Statin drugs — synonyms: statin, "HMG-CoA reductase inhibitor", simvastatin,
   atorvastatin, lovastatin, pravastatin
2. Causal relationship — cause, risk, association, induce, "increase risk"
3. Breast cancer — "breast cancer", "breast carcinoma", "breast neoplasm"

Strategy: AND the three concept groups; use OR to expand synonyms within each.
Phrase-quote multi-word terms; keep AND chains short to avoid zero-result queries.
</reasoning>
<query>(statin OR "HMG-CoA reductase inhibitor" OR simvastatin OR atorvastatin OR lovastatin)
AND (cause OR risk OR association OR induce)
AND ("breast cancer" OR "breast carcinoma" OR "breast neoplasm")</query>
```

Compare to [GRPO v1](Supreeth/searchlm-nl2bm25-grpo)'s output for the same query:
```
<reasoning>
</reasoning>
<query>Cholesterol Statin Breast Cancer</query>
```

GRPO v2 generates 147-token completions with substantive reasoning; GRPO v1 generated 5-token
keyword bags with empty reasoning blocks.

---

## How v2 eliminated reward hacking

The v1 reward (`0.6 × NDCG@10 + 0.4 × MRR`) was gameable with keyword bags on small corpora
because BM25 recall on 3–5K doc indexes is high for distinctive nouns. Three mechanisms
closed this gap in v2:

```python
# v2 reward function
base   = 0.6 * max(0, ndcg_at_10 - keyword_baseline_ndcg)  # must beat noun-extraction
       + 0.4 * mrr
shaped = complexity_mult * base                              # 1.0 with boolean ops, 0.5 without
       + 0.15 * min(reasoning_tokens / 100, 1.0)            # up to +0.15 reasoning bonus
reward = 0.0 if len(query.split()) < 3 else shaped          # hard gate: ≥3 tokens required
```

| Mechanism | Effect |
|-----------|--------|
| Keyword baseline delta | Model earns zero NDCG credit for matching naive noun-extraction |
| Hard length gate | Single/double-word queries unconditionally return 0.0 |
| Reasoning depth bonus | Up to +0.15 reward for ≥100-token reasoning blocks |
| Complexity multiplier | Queries without boolean operators earn half credit |

---

## All SearchLM checkpoints

| Model | NFCorpus NDCG@10 | SciFact NDCG@10 | Mean tokens | Boolean ops |
|-------|-----------------|----------------|-------------|-------------|
| base (Qwen2.5-3B-Instruct) | 0.455 | 0.386 | 120 | ~20% |
| [SFT v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft) | 0.441 | 0.273 | 95 | ~80% |
| [GRPO v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo) ⚠️ | 0.556 | 0.608 | **5–7** | **0%** |
| [SFT v2](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft-v2) | 0.466 | 0.358 | 109 | ~65% |
| [**GRPO v2**](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo-v2) ✅ | **0.577** | **0.657** | 147 | ~35% |

Evaluated on BEIR test splits (NFCorpus: 323 queries, SciFact: 300 queries).

---

## Behavioral comparison (GRPO v1 vs GRPO v2)

| Dimension | [GRPO v1](Supreeth/searchlm-nl2bm25-grpo) ⚠️ | **GRPO v2** ✅ |
|-----------|---------|---------|
| NFCorpus NDCG@10 | 0.556 | **0.577** (+0.021) |
| SciFact NDCG@10 | 0.608 | **0.657** (+0.049) |
| Mean completion length | **5–7 tokens** | 147 tokens |
| Boolean operator usage | **0%** | ~35% |
| Phrase usage | **0%** | ~25% |
| `frac_reward_zero_std` (step 1) | **90–96%** | 0.0% |
| `frac_reward_zero_std` (final) | **90–96%** | ~61% |
| Reasoning block | **empty** | substantive |

The shaped reward did not sacrifice performance to eliminate gaming — it improved both.

---

## Training Details

| Setting | Value |
|---------|-------|
| Base model | [searchlm-nl2bm25-sft-v2](Supreeth/searchlm-nl2bm25-sft-v2) |
| Method | GRPO (TRL GRPOTrainer + vLLM colocate, single H100) |
| Reward | Shaped: `complexity_mult × (0.6 × ΔNDCG + 0.4 × MRR) + 0.15 × reasoning_depth` |
| Training datasets | NFCorpus + SciFact + FiQA-2018 (3K queries, 57,638 docs) |
| `num_generations` | 8 (was 2 in v1) |
| Epochs | 1 |
| Steps | 2,879 (~3.3s/step) |
| Batch size | 2 (+ 8 grad accum = effective 16) |
| Learning rate | 1e-6 |
| vLLM GPU utilisation | 0.30 (24 GB KV cache) |
| Max new tokens | 1,024 |
| Gradient checkpointing | yes |
| Hardware | NVIDIA H100 80 GB |
| Training time | ~3h 3m |
| Final train loss | 0.0012 |
| Final mean reward | ~0.29 |
| W&B run | `supreethrao/searchlm/runs/9x1tg52j` |

### Why these hyperparameters

**`num_generations=8`**: v1 used 2, leading to 90-96% of groups having zero within-group
reward variance (no gradient signal). With 8 completions, variance emerged from step 1.

**`vllm_gpu_memory_utilization=0.30`**: On H100 80GB, Adam fp32 optimizer states for a 3B
model require ~24 GB. At 0.45 utilisation, vLLM reserved 36 GB and Adam states OOM'd. 0.30
leaves 56 GB for the training stack.

**`torch_compile=False`**: Compiled backward pass materialised fp32 FFN intermediate buffers
(~90 MB each) that eager + gradient checkpointing avoids, causing OOM at batch_size=4.

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Supreeth/searchlm-nl2bm25-grpo-v2",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Supreeth/searchlm-nl2bm25-grpo-v2")

SYSTEM_PROMPT = """You are an expert information retrieval specialist. Convert the \
natural language query into a Tantivy boolean search query.

Output format (strictly follow this):
<reasoning>
Step-by-step concept extraction and synonym expansion.
</reasoning>
<query>your boolean query here</query>"""

nl_query = "effects of climate change on coral reef ecosystems"
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"Convert to a Tantivy boolean search query:\n\n{nl_query}"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

---

## Tantivy Boolean Syntax

[Tantivy](https://github.com/quickwit-oss/tantivy) is a full-text search engine library.
The model targets its query language:

| Construct | Syntax | Example |
|-----------|--------|---------|
| Single term | `word` | `cancer` |
| Exact phrase | `"phrase"` | `"bone density"` |
| AND | `A AND B` | `vitamin AND calcium` |
| OR | `A OR B` | `cancer OR tumor OR malignancy` |
| NOT | `NOT A` | `NOT review` |
| Grouping | `(A OR B)` | `(cat OR feline) AND behavior` |
| Field scope | `field:term` | `title:"machine learning"` |
| Boost | `term^N` | `cancer^2 OR tumor` |

---

## Related resources

- **Code:** [SupreethRao99/searchLM](https://github.com/SupreethRao99/searchLM)
- **Analysis:** [Full five-checkpoint comparison report](https://github.com/SupreethRao99/searchLM/blob/main/REWARD_HACKING_REPORT_V2.md)
- **Dataset:** [Supreeth/nl2bm25-sft](https://huggingface.co/datasets/Supreeth/nl2bm25-sft)
- **Collection:** [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm)

## Citation

```bibtex
@misc{searchlm2026,
  title  = {SearchLM: Training Small Language Models for Boolean Query Generation via RLVR},
  author = {Rao, Supreeth},
  year   = {2026},
  url    = {https://github.com/SupreethRao99/searchLM},
}
```