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Model: Supreeth/searchlm-nl2bm25-grpo-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+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},
+}
+```

added_tokens.json

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+  "<tool_call>": 151657,
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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.6"
+}

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+    },
+    "151660": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "151662": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": null
+}