Update README.md

README.md

@@ -339,14 +339,19 @@ Upload a PDF file of the recent DeepSeek-R1 paper and ask the following question
   App Answer: RL stands for Reinforcement Learning, a type of machine learning where an agent learns to make decisions by performing actions and receiving feedback from those actions in the form of rewards or penalties. This feedback helps the agent understand which actions lead to favorable outcomes. In the context of improving reasoning capabilities, RL helps by allowing language models (LMs) to practice and refine their ability to solve complex problems through a process of trial and error. By applying RL, models like DeepSeek-R1-Zero can develop sophisticated reasoning behaviors without relying on supervised fine-tuning (SFT) with pre-labeled data sets. This is significant because it means the models can improve their reasoning capabilities by generating and evaluating their own solutions to problems, leading to self-improvement over time. The application of RL enables the model to explore different "chain-of-thought" (CoT) processes for solving complex reasoning tasks. These CoT processes involve the model generating intermediate steps or explanations that lead to a final answer, mimicking a form of human-like reasoning. Through RL, the model receives feedback on the effectiveness of its reasoning processes, allowing it to adjust and improve its approach to problem-solving over time. This approach has demonstrated significant improvements in reasoning tasks, as evidenced by the performance of DeepSeek-R1-Zero on reasoning benchmarks. By focusing on self-evolution through a pure RL process, models can develop powerful reasoning capabilities, showcasing advanced behaviors such as self-verification, reflection, and the generation of long CoTs. Essentially, RL helps in reasoning by incentivizing models to autonomously discover and refine effective reasoning patterns, aligning closer with human-like problem-solving skills.
   Vibe Check Failed: Response from my react app took a bit too long and was very large
 
-3. What is the difference between DeepSeek-R1 and DeepSeek-R1-Zero?
+2. What is the difference between DeepSeek-R1 and DeepSeek-R1-Zero?
    App Response: The primary difference between DeepSeek-R1 and DeepSeek-R1-Zero as outlined in the context is in their approach and capabilities: 1. **DeepSeek-R1-Zero** is focused on leveraging extended test-time computation to solve reasoning tasks. It naturally learns to solve increasingly complex reasoning tasks by generating hundreds to thousands of reasoning tokens, which allows for more in-depth exploration and refinement of thought processes. This includes sophisticated behaviors like reflection and exploring alternative problem-solving approaches. This capability is an intrinsic development within the model, achieved without external adjustments. 2. **DeepSeek-R1**, on the other hand, is described as more powerful due to its utilization of cold-start data alongside iterative reinforcement learning (RL) fine-tuning. This approach enables DeepSeek-R1 to achieve performance comparable to OpenAI-o1-1217 across various tasks. It represents an advancement in leveraging machine learning techniques to improve performance on a wide range of tasks, including those that involve reasoning capabilities. In summary, DeepSeek-R1-Zero emphasizes self-evolution and the natural development of reasoning capabilities through extended test-time computation. In contrast, DeepSeek-R1 leverages cold-start data and iterative RL fine-tuning to enhance its performance, making it more powerful and capable across a broader range of tasks.
-   Vibe Check: Response was faster but still very drawn out would like for it to be shorter more summarized
-5. What is this paper about?
+   
+3. What is this paper about?
+   
     App Response: Based on the provided context, it's challenging to pinpoint a single overarching theme or subject of the paper since the context includes references to multiple works and authors across various areas of research. However, several key themes emerge from the references listed: 1. **Large Language Models (LLMs) and Software Engineering:** Works like "Agentless: Demystifying llm-based software engineering agents" suggest a focus on exploring how LLMs can be utilized in software engineering without traditional agents, potentially offering new insights into automated coding, debugging, or other software development processes. 2. **Mathematical Reasoning and Problem-Solving:** References to works like "Math-shepherd: A label-free step-by-step verifier for llms in mathematical reasoning" and "Deepseekmath: Pushing the limits of mathematical reasoning in open language models" indicate significant attention to enhancing the capability of LLMs in understanding and solving mathematical problems, including complex reasoning and proof verification. 3. **Evaluation and Benchmarking of AI Models:** Several references, such as the evaluation of large language models trained on code and the creation of more robust multi-task language understanding benchmarks, point towards efforts to critically evaluate and improve the performance of AI models across various tasks, including code understanding, multi-task language comprehension, and more. 4. **Reinforcement Learning and Game Theory:** Mentions of works like "Mastering chess and shogi by self-play with a general reinforcement learning algorithm" suggest an interest in applying reinforcement learning techniques to master complex games, which could indicate broader research into advanced AI strategies and decision-making processes. 5. **Instruction-following Evaluation for Large Language Models:** The reference to instruction-following evaluation highlights research aimed at assessing how well large language models can follow specific instructions, which is crucial for their practical application in diverse fields. Given the wide range of topics covered in the listed references, the paper likely touches on several cutting-edge areas in AI research, including but not limited to the development, evaluation, and application of large language models in software engineering, mathematical reasoning, and beyond. Without more specific details or a clear abstract, it's difficult to determine a singular focus from the context provided.
-    Vibe Check: Failed it's a bit too incoherent and drawn out would have to make adjustments to the prompt and add a user_template to fix all 3 answers to be more concise
 
 Does this application pass your vibe check? Are there any immediate pitfalls you're noticing?
+1. Vibe Check Failed: Response from my react app took a bit too long and was very large
+2. Vibe Check: Response was faster but still very drawn out would like for it to be shorter more summarized
+3. Vibe Check: Failed it's a bit too incoherent and drawn out would have to make adjustments to the prompt and add a user_template to fix all 3 answers to be more concise
+4. File size is only 2mb if the paper was any bigger the file upload would fail
+5. Generated too many chuncks if i added meta data support it could improve performance because of the 200 page paper theres just a lot of data that goes back and forth
 
 ## 🚧 CHALLENGE MODE 🚧