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Model: sailing-lab/SR2AM-v0.1-8B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- agent
+- reasoning
+- tool-use
+- simulative-planning
+base_model: Qwen/Qwen3-8B
+---
+
+# SR²AM-v0.1-8B
+
+![SR²AM Illustration](model.png)
+
+We argue that efficient agentic reasoning benefits from decomposing deliberation into three interacting systems: **reactive execution** (System I) for fine-grained reasoning and direct action; **simulative reasoning** (System II) that predicts consequences of proposed actions through a world model; and **self-regulation** (System III) that decides *when* and *how deeply* to plan through a learned **configurator**.
+
+**SR²AM** (Self-Regulated Simulative Reasoning Agentic LLM) is our instantiation: the configurator and simulative planner are realized as distinct stages within an LLM's chain-of-thought reasoning, with the LLM itself serving as the world model in language space.
+
+SR²AM-v0.1-8B achieves an overall Pass@1 of **57.0** across 11 benchmarks spanning math, science, tabular analysis, and web information seeking — competitive with systems at 120–355B parameters.
+
+More details: [project website](https://sailing-lab.github.io/sr2am-self-regulated-planning) | [paper](https://arxiv.org/abs/2605.22138) | [GitHub](https://github.com/sailing-lab/sr2am).
+
+
+## Key Features
+
+- **System I + II + III decomposition**: a configurator (System III) decides per-turn whether to plan, continue an existing plan, or act directly; a simulative planner (System II) constructs plans grounded in predicted future states; reactive execution (System I) handles fine-grained reasoning and tool use.
+- **SFT + RL training**: supervised learning on data encoding the self-regulated planning structure, followed by reinforcement learning (GRPO) for task success.
+- **Agentic tool use**: web search (SerpAPI), web browsing with LLM summarization, and stateless Python code execution (SandboxFusion).
+- **Compact and efficient**: 3,698 reasoning tokens per trajectory on average — fewer or comparable to other systems at the same scale while outperforming them in Pass@1.
+
+## Quick Start
+
+See the [GitHub repository](https://github.com/sailing-lab/sr2am) for setup and inference instructions.
+
+## Main Results
+
+![Pass@1 vs. parameter size and reasoning-token count](main-results.png)
+
+SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the [paper](https://arxiv.org/abs/2605.22138).
+
+## Citation
+
+```bibtex
+@article{deng2026sr2am,
+  title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
+  author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
+          Pimpalkhute, Varad and Killian, Taylor W. and
+          Liu, Zhengzhong and Xing, Eric P.},
+  journal={arXiv preprint arXiv:2605.22138},
+  year={2026}
+}
+```
+
+## License
+
+Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
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+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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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 + '\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\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 %}
+
+        {{- '<|im_start|>' + message.role }}
+        {% generation %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- ('' + reasoning_content.strip('\n') + '\n\n' + content.lstrip('\n')).lstrip('\n') }}
+            {%- else %}
+                {{- content }}
+            {%- endif %}
+        {%- else %}
+            {{- 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|>' }}
+        {% endgeneration %}
+    {%- 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,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
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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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "do_sample": true,
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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.55.2"
+}

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