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Model: Jinyang23/OPID-ALFWorld-1.7B
Source: Original Platform

.gitattributes

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen3-1.7B
+library_name: transformers
+pipeline_tag: reinforcement-learning
+tags:
+- agent
+- reinforcement-learning
+- long-horizon
+- embodied-ai
+- skill-distillation
+- alfworld
+- opid
+- qwen3
+---
+
+# OPID-ALFWorld-1.7B
+
+This model is trained using the **OPID** framework proposed in the paper:
+
+**OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning**
+
+- Paper: [https://arxiv.org/abs/2606.26790](https://arxiv.org/abs/2606.26790)
+- Hugging Face paper page: [https://huggingface.co/papers/2606.26790](https://huggingface.co/papers/2606.26790)
+- Code: [jinyangwu/OPID](https://github.com/jinyangwu/OPID)
+
+## Overview
+
+OPID is an **On-Policy Skill Distillation** framework for long-horizon language
+agents. It turns completed on-policy trajectories into hierarchical hindsight
+skills and uses them as dense training supervision. During inference, the policy
+acts directly from the environment history without an analyzer, skill retrieval,
+or privileged context.
+
+This checkpoint is the **ALFWorld Qwen3-1.7B** model trained with OPID.
+
+## Key Features
+
+- **On-policy skill distillation:** OPID distills behavioral knowledge from the
+  model's own completed trajectories instead of relying on offline demonstrations.
+- **Hierarchical hindsight skills:** Episode-level skills capture global
+  workflows or failure-avoidance rules, while step-level skills capture local
+  critical decisions.
+- **Critical-first routing:** Step-level skills are used at identified critical
+  timesteps, and episode-level skills are used elsewhere.
+- **Dense token-level supervision:** OPID re-scores the same sampled response
+  under original and skill-augmented contexts, turning the log-probability shift
+  into a self-distillation advantage.
+- **No inference overhead:** The released policy does not require an analyzer,
+  skill memory, retrieval module, or extra privileged prompt at test time.
+
+## Performance Highlights
+
+On **ALFWorld with Qwen3-1.7B-Instruct**, OPID achieves the best average score
+among the compared methods in our results.
+
+| Method | Pick | Look | Clean | Heat | Cool | Pick2 | Avg. |
+|--------|------|------|-------|------|------|-------|------|
+| Vanilla | 25.0 | 22.2 | 3.1 | 0.0 | 21.4 | 4.2 | 12.5 |
+| Skill-Prompt* | 10.3 | 50.0 | 16.1 | 0.0 | 0.0 | 5.0 | 9.4 |
+| OPSD | 26.3 | 33.3 | 9.1 | 0.0 | 4.5 | 5.3 | 14.1 |
+| GRPO | 71.1 | 41.7 | 36.4 | 40.0 | 31.8 | 31.6 | 46.1 |
+| Skill-GRPO | 27.6 | 54.5 | 22.7 | 27.3 | 0.0 | 19.2 | 21.1 |
+| Skill-GRPO* | 31.4 | 42.9 | 51.9 | 8.3 | 11.5 | 7.1 | 28.1 |
+| GRPO+OPSD | 38.2 | 50.0 | 30.8 | 28.6 | 30.0 | 21.1 | 32.0 |
+| Skill-SD | 52.9 | 37.5 | 69.2 | 42.9 | 60.0 | 36.8 | 52.3 |
+| RLSD | 50.0 | 37.5 | 61.5 | 19.0 | 50.0 | 21.1 | 42.2 |
+| SDAR | 73.5 | 25.0 | 76.9 | 33.3 | 40.0 | 36.8 | 53.9 |
+| **OPID** | **65.9** | **72.7** | **66.7** | **40.0** | **63.2** | **45.0** | **58.9** |
+
+Numbers are success rates from the ALFWorld Qwen3-1.7B-Instruct block in the
+OPID results figure.
+
+## Quickstart
+
+Here is a standard Transformers loading example:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Jinyang23/OPID-ALFWorld-1.7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "You are in an ALFWorld environment. Decide the next valid action."
+
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt},
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512,
+)
+generated_ids = [
+    output_ids[len(input_ids):]
+    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## ALFWorld Evaluation
+
+The OPID repository provides local vLLM evaluation scripts for ALFWorld. After
+installing the repository and ALFWorld dependencies, run:
+
+```bash
+git clone https://github.com/jinyangwu/OPID.git
+cd OPID
+
+MODEL_PATH=Jinyang23/OPID-ALFWorld-1.7B \
+MODEL_NAME=OPID-ALFWorld-1.7B \
+bash examples/prompt_agent/run_local_vllm_alfworld.sh
+```
+
+You can also serve the model manually with vLLM:
+
+```bash
+vllm serve Jinyang23/OPID-ALFWorld-1.7B \
+  --served-model-name OPID-ALFWorld-1.7B \
+  --dtype bfloat16
+```
+
+## Model Details
+
+- **Base Model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+- **Training Method:** OPID (On-Policy Skill Distillation)
+- **Training Environment:** ALFWorld
+- **Model Architecture:** Qwen3ForCausalLM
+- **Precision:** bfloat16
+- **Context Length:** 40960 tokens in the released config
+
+## Limitations
+
+- This checkpoint is specialized for ALFWorld-style text interaction tasks.
+- It may output invalid actions when used outside the ALFWorld prompting format.
+- The model inherits limitations from the Qwen3 base model and from RL training
+  in simulated environments.
+- This release is intended for research use in language-agent evaluation.
+
+## Citation
+
+If you use this model or the OPID framework in your research, please cite:
+
+```bibtex
+@misc{yang2026opid,
+  title={OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning},
+  author={Shuo Yang and Jinyang Wu and Zhengxi Lu and Yuhao Shen and Fan Zhang and Lang Feng and Shuai Zhang and Haoran Luo and Zheng Lian and Zhengqi Wen and Jianhua Tao},
+  year={2026},
+  eprint={2606.26790},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2606.26790}
+}
+```
+
+## Links
+
+- Paper: https://arxiv.org/abs/2606.26790
+- Hugging Face paper page: https://huggingface.co/papers/2606.26790
+- Code: https://github.com/jinyangwu/OPID
+- Model: https://huggingface.co/Jinyang23/OPID-ALFWorld-1.7B

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

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 %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "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",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.3"
+}

model.safetensors

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+oid sha256:f5c99307f85700aa7278965edccc339818b81cc3cbffa35934a4ce6cd1bb363b
+size 4063515640

special_tokens_map.json

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+{
+  "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|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|im_start|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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