| --- |
| base_model: openbmb/MiniCPM5-1B |
| library_name: peft |
| pipeline_tag: text-generation |
| tags: |
| - lora |
| - browser-agent |
| - reasoning |
| - planning |
| - build-small |
| - peft |
| --- |
| |
| # TinyBrowserPlanner-Reason |
|
|
| ## A 1B model can explain the correct browser action before it can reliably choose it. |
|
|
| This repository contains a LoRA adapter trained on top of MiniCPM5-1B for lightweight browser planning tasks. |
|
|
| The original goal was simple: |
|
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| Can a 1B model decide the next browser action given a task and an observation? |
|
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| Actions include: |
|
|
| * search |
| * open_page |
| * extract |
| * back |
| * finish |
| * refine_search |
|
|
| ## Key Findings |
|
|
| ### 1. Data quality beats data quantity |
|
|
| Adding large amounts of similar trajectory data produced almost no improvement. |
|
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| However, adding only ~200 carefully designed hard examples significantly improved replanning behavior. |
|
|
| ### 2. Adding actions creates both capability and confusion |
|
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| Introducing the `back` action allowed the model to recover from wrong pages and paywalls. |
|
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| However, the model quickly learned to overuse `back` as a universal solution. |
|
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| ### 3. Reason-First training dramatically improves planning |
|
|
| Action-only planning: |
|
|
| 4/12 |
|
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| Reason-First planning: |
|
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| 10/12 |
|
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| Using only 40 reasoning examples and less than 10 seconds of additional training. |
|
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| The most important result: |
|
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| The model already understood the state of the environment. |
|
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| It failed because it learned shortcut action heuristics. |
|
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| Forcing the model to explicitly generate a reason before selecting an action dramatically improved decision quality. |
|
|
| ## Example |
|
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| Task: |
|
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| Find Apple stock price |
|
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| Observation: |
|
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| Price displayed prominently on page. |
|
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| Reason: |
|
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| The requested information is already available. |
|
|
| Action: |
|
|
| extract |
|
|
| --- |
|
|
| Task: |
|
|
| Find CEO of OpenAI |
|
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| Observation: |
|
|
| Page discusses Microsoft CEO. |
|
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| Reason: |
|
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| The page is irrelevant to the requested information. |
|
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| Action: |
|
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| back |
|
|
| ## Training |
|
|
| Base model: |
|
|
| openbmb/MiniCPM5-1B |
|
|
| Method: |
|
|
| LoRA fine-tuning |
|
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| Framework: |
|
|
| Unsloth + PEFT |
|
|
| ## Limitations |
|
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| The model performs well on simple browser planning and replanning scenarios. |
|
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| However, it still struggles with: |
|
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| * multi-step recovery chains |
| * long-horizon planning |
| * complex search strategy generation |
| * comparison tasks requiring multiple sources |
|
|
| ## Conclusion |
|
|
| This project suggests that explicit reasoning may act as a lightweight regularizer for small planning models. |
|
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| A 1B model can often explain the correct action before it can reliably choose it. |
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| This repository contains only the LoRA adapter. |
|
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| The base model must be downloaded separately. |
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|
