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Model: anurag203/clarify-rl-run4-qwen3-1.7b-beta0.2
Source: Original Platform

.gitattributes

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+base_model_relation: finetune
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation
+- conversational
+- reinforcement-learning
+- rlhf
+- rl
+- grpo
+- trl
+- kl-anchor
+- agentic
+- tool-use
+- clarifying-questions
+- ask-vs-guess
+model-index:
+- name: clarify-rl-run4-qwen3-1.7b-beta0.2
+  results:
+  - task:
+      type: text-generation
+      name: ClarifyRL multi-turn ask-or-guess
+    dataset:
+      type: held_out_scenarios
+      name: ClarifyRL eval (50 held-out scenarios, n=50 v4)
+    metrics:
+    - type: avg_score
+      value: 0.0560
+      name: avg_score (μ over 4 families × 50 scenarios)
+    - type: completion_rate
+      value: 0.14
+      name: completion_rate
+    - type: family_event_planning_mean
+      value: 0.175
+      name: event_planning μ
+    - type: family_meeting_scheduling_mean
+      value: 0.064
+      name: meeting_scheduling μ
+---
+
+# ClarifyRL — Run 4 — Qwen3-1.7B GRPO (β=0.2 KL anchor)
+
+> **The hero checkpoint of the ClarifyRL hackathon submission.** This is
+> Qwen3-1.7B trained with TRL GRPO and an explicit **KL anchor at β=0.2**
+> against the frozen base, which is the single change that turned a
+> capability collapse (Run 2, β=0) into a measurable improvement on the
+> held-out eval *while preserving breadth across families*.
+
+- **Live demo (replay + CPU live chat):** [`anurag203/clarify-rl-demo`](https://huggingface.co/spaces/anurag203/clarify-rl-demo)
+- **Code (training, eval, plots):** [`github.com/anurag203/clarify-rl`](https://github.com/anurag203/clarify-rl)
+- **W&B dashboard (all 3 runs live):** [`anuragagarwal203-cisco/clarify-rl`](https://wandb.ai/anuragagarwal203-cisco/clarify-rl)
+- **Environment Space (OpenEnv MCP server):** [`agarwalanu3103/clarify-rl`](https://huggingface.co/spaces/agarwalanu3103/clarify-rl)
+- **Hackathon write-up (blog):** [`docs/blog.md`](https://github.com/anurag203/clarify-rl/blob/main/docs/blog.md)
+
+## Model summary
+
+| Field | Value |
+| --- | --- |
+| Base model | [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) (NOT the instruct tune) |
+| Algorithm | TRL GRPO (Group Relative Policy Optimization) |
+| KL anchor (β) | **0.2** (vs Run 2 = 0.0) |
+| Learning rate | 5e-7, cosine decay → 1.7e-9 |
+| Steps | 300 |
+| Wall time | 78.2 min on a single A100 (HF Jobs `a100-large`) |
+| Optimizer | adamw_torch_fused |
+| Generations / step | 4 (with vLLM sampler) |
+| Max completion length | 768 tokens |
+| Reward stack | `OutputCorrectnessRubric` (0.6) + `EfficiencyRubric` (0.2) + `FormatCheckRubric` (0.2) over 5 task families |
+| Cost | **~$1.80** of HF Jobs credit |
+
+The training and eval are fully reproducible from
+[`training/train_grpo.py`](https://github.com/anurag203/clarify-rl/blob/main/training/train_grpo.py)
+with `BETA=0.2 LEARNING_RATE=5e-7 NUM_STEPS=300`.
+
+## What this model does
+
+ClarifyRL trains a small LLM to **ask before it acts**. Given a deliberately
+under-specified user request (e.g. *"set up a celebration"*), the agent has
+6 question budget and must choose between:
+
+- `ask_question(question)` — pull a single missing field from the user simulator
+- `propose_plan(plan)` — emit the final structured JSON plan
+- `get_task_info()` — re-read the brief
+
+Reward depends on (a) format correctness, (b) field overlap of the final
+plan vs the hidden ground-truth profile, and (c) efficiency (fewer
+questions for higher-quality plans). 5 task families: `event_planning`,
+`medical_intake`, `meeting_scheduling`, `support_triage`,
+`coding`. Held-out eval uses 50 scenarios with the same 4-family coverage
+as training.
+
+## The headline result — KL anchor turns the model around
+
+Same base, same data, same step count. The only difference is β.
+
+| Eval metric (n=50, held-out) | 1.7B base | Run 2 (β=0) | **Run 4 (β=0.2) ✅** |
+| --- | ---: | ---: | ---: |
+| avg_score (μ across 4 families) | 0.067 | 0.029 ↓ | **0.056** ✅ |
+| completion_rate | 0.18 | 0.06 | **0.14** |
+| event_planning μ | 0.138 | **0.000 ❌** | **0.175 ✅✅** |
+| event_planning max | 0.522 | 0.000 | 0.510 |
+| meeting_scheduling μ | 0.153 | 0.130 | 0.064 ↓ |
+| meeting_scheduling max | 0.500 | **0.725** | 0.350 |
+
+Three observations the hackathon write-up leans on:
+
+1. **GRPO without anchor causes capability collapse.** Run 2 (β=0) drove
+   `event_planning` from 0.138 → **0.000** mean while inflating one peak
+   in `meeting_scheduling`. The model traded breadth for an
+   exploit-and-overfit that the held-out eval flags immediately.
+2. **GRPO with KL anchor cleanly improves the protected family.**
+   Run 4 (β=0.2, lr=5e-7) on the same model recovered avg_score to
+   **0.056** AND **beat the base on `event_planning`** (0.138 → **0.175**).
+   The anchor literally fixed Run 2's regression *without* extra data.
+3. **The cost is peak capability.** Run 4 dropped
+   `meeting_scheduling` max from 0.725 (Run 2's gem) to 0.350. KL prevents
+   the kind of extreme specialization Run 2 leaned on. That's the
+   trade-off, stated honestly.
+
+For the same-base delta plot
+([`plots/06_same_base_delta.png`](https://raw.githubusercontent.com/anurag203/clarify-rl/main/plots/06_same_base_delta.png)),
+see the full blog at
+[`docs/blog.md`](https://github.com/anurag203/clarify-rl/blob/main/docs/blog.md).
+
+## Intended use
+
+- **Research / hackathon** — reproduce the KL-anchor ablation on a small
+  reasoner.
+- **Demo / education** — illustrate that a 1.7B param model can be steered
+  toward an *ask-first* policy with a tiny RL budget when KL is enforced.
+- **Drop-in replacement for `Qwen/Qwen3-1.7B`** in agentic, multi-turn,
+  tool-using settings where the agent should clarify ambiguous requests
+  rather than hallucinate fields.
+
+## Out-of-scope use
+
+- General chat assistant. The reward shaping is highly specific to the
+  ClarifyRL ask-or-guess setting; do not expect calibration or RLHF-style
+  helpfulness on open-ended prompts.
+- Production / safety-critical / medical / legal. The model has **no** RLHF
+  safety alignment; the `medical_intake` family in the eval is a *task
+  scaffold*, not a clinical reasoning task.
+- Anything outside English.
+
+## How to use it
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "anurag203/clarify-rl-run4-qwen3-1.7b-beta0.2"
+tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
+mdl = AutoModelForCausalLM.from_pretrained(
+    repo,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+SYSTEM = (
+    "You are an agent that must complete a task by asking clarifying "
+    "questions and then proposing a structured JSON plan. Tools: "
+    "ask_question(question), propose_plan(plan), get_task_info(). "
+    "You have a 6-question budget. Output ONLY one tool call per turn."
+)
+USER = "Set up a celebration."
+
+prompt = tok.apply_chat_template(
+    [{"role": "system", "content": SYSTEM}, {"role": "user", "content": USER}],
+    tokenize=False,
+    add_generation_prompt=True,
+    chat_template_kwargs={"enable_thinking": False},
+)
+out = mdl.generate(
+    **tok(prompt, return_tensors="pt").to(mdl.device),
+    max_new_tokens=120,
+    temperature=0.7,
+    do_sample=True,
+    pad_token_id=tok.eos_token_id,
+)
+print(tok.decode(out[0][tok(prompt, return_tensors="pt")["input_ids"].shape[1]:],
+                 skip_special_tokens=True))
+# expected first turn: ask_question("What is the date of the celebration?")
+```
+
+For the full multi-turn agent loop with tool parsing, see
+[`scripts/eval_agent.py`](https://github.com/anurag203/clarify-rl/blob/main/scripts/eval_agent.py)
+or the live tab of the
+[`anurag203/clarify-rl-demo`](https://huggingface.co/spaces/anurag203/clarify-rl-demo) Space.
+
+## Training data
+
+Held-out eval uses
+[`scenarios/eval_held_out.json`](https://github.com/anurag203/clarify-rl/blob/main/scenarios/eval_held_out.json)
+(50 scenarios across 4 families). The training data is procedurally generated
+inside the OpenEnv MCP environment and is *not* a static dump — the user
+simulator answers each `ask_question` with a sampled ground-truth profile,
+so the same scenario can yield different conversations across rollouts.
+
+See
+[`scenarios/`](https://github.com/anurag203/clarify-rl/tree/main/scenarios)
+and
+[`docs/05-scenario-design.md`](https://github.com/anurag203/clarify-rl/blob/main/docs/05-scenario-design.md)
+for the full scenario taxonomy.
+
+## Training procedure
+
+| Hyperparameter | Value |
+| --- | --- |
+| Algorithm | GRPO (TRL ≥ 1.0) |
+| `beta` (KL coefficient) | **0.2** |
+| Learning rate | 5e-7 (cosine to ~1.7e-9) |
+| Total steps | 300 |
+| Generations / step | 4 |
+| Group size | 4 |
+| Max completion length | 768 |
+| Sampler | vLLM, `temperature=1.0`, `top_p=1.0` |
+| Reference model | Frozen `Qwen/Qwen3-1.7B` (loaded once, never updated) |
+| Hardware | 1× A100 80GB (HF Jobs `a100-large`) |
+| Wall time | 78.2 min |
+| Final reward | 0.0050 mean / 0.114 max |
+| KL stayed bounded | 0.005 – 0.010 throughout (the anchor did its job) |
+| LR cosine-decayed | yes, to 1.7e-9 (verified in W&B) |
+
+The full 300-entry `log_history.json` is committed at
+[`outputs/run_artifacts/1.7B-KL/`](https://github.com/anurag203/clarify-rl/tree/main/outputs/run_artifacts/1.7B-KL)
+and is mirrored to
+[`wandb.ai/anuragagarwal203-cisco/clarify-rl`](https://wandb.ai/anuragagarwal203-cisco/clarify-rl)
+under run name `run4-1p7b-kl-anchor`.
+
+## Evaluation
+
+| Family | 1.7B base μ | Run 2 (β=0) μ | **Run 4 (β=0.2) μ** |
+| --- | ---: | ---: | ---: |
+| event_planning | 0.138 | 0.000 | **0.175** |
+| meeting_scheduling | 0.153 | 0.130 | 0.064 |
+| medical_intake | 0.000 | 0.000 | 0.000 |
+| support_triage | 0.000 | 0.000 | 0.000 |
+| **avg_score (μ)** | **0.067** | **0.029** | **0.056** |
+| **completion_rate** | 18% | 6% | **14%** |
+
+Eval methodology: 50 held-out scenarios per family, 5 families, scored by
+the same rubric stack used at training time. Eval runs are reproducible
+from
+[`scripts/run_eval.py`](https://github.com/anurag203/clarify-rl/blob/main/scripts/run_eval.py)
+with the JSON profile committed at
+[`outputs/run_artifacts/1.7B-KL/evals/`](https://github.com/anurag203/clarify-rl/tree/main/outputs/run_artifacts/1.7B-KL).
+
+## Limitations
+
+- 1.7B parameters — not a strong reasoner. Even Run 4's mean (0.056) is
+  below 4B base's mean (0.145). RL helped, but parameter count still wins.
+- Format-pass rate is **0%** across all evaluated runs because the eval
+  rubric expects strict JSON keys; even Run 4 occasionally proposes plans
+  that don't quite match the schema. We deliberately leave the format
+  rubric strict because format failure is a common failure mode of small
+  RLHF'd models.
+- `medical_intake` and `support_triage` remain at 0 across all 1.7B and
+  0.6B variants — these families need either a stronger base or scenario
+  redesign. Logged as future work in
+  [`docs/blog.md` §7b](https://github.com/anurag203/clarify-rl/blob/main/docs/blog.md).
+- 4B GRPO (Run 3) was canceled in HF Jobs queue at 48 min — the anchor
+  finding has not yet been confirmed at 4B scale.
+
+## Citation
+
+If you build on this, please cite the GitHub repository and the W&B
+project:
+
+```bibtex
+@misc{agarwal2026clarifyrl,
+  author       = {Agarwal, Anurag},
+  title        = {ClarifyRL: Teaching small LLMs to ask before they act,
+                  with KL-anchored GRPO},
+  year         = {2026},
+  howpublished = {\url{https://github.com/anurag203/clarify-rl}},
+  note         = {Hackathon submission, Apr 26 2026.}
+}
+```
+
+Built on top of [TRL](https://github.com/huggingface/trl) (GRPO trainer),
+[Qwen3](https://huggingface.co/Qwen) (base model), and the [OpenEnv MCP
+environment](https://huggingface.co/spaces/agarwalanu3103/clarify-rl).
+
+## License
+
+Apache-2.0 — same as the upstream Qwen3-1.7B base.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

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