RLHF for News Generation

Analyze https://GitHub com/kliewerdaniel/news17.git and I want to shape the prompts by having a state for the generation that would be modified by rlhf

You’re working with **ssh://github.com/kliewerdaniel/news17.git**, an evolution of earlier “infinite news” projects (like news16 and news17) that fetches from RSS feeds, dynamically crafts news scripts, and synthesizes a continuous “broadcast” — likely with text-to-speech and stream integration citeturn1search0turn0search2. --- ## 🛠 What’s Under the Hood 1. **Feed Ingestion** A set of RSS/Atom feeds are polled, downloaded, and parsed. 2. **Content Pipeline** Articles are filtered (e.g., by recency or keywords), then merged/summarized. 3. **Prompt Architecture** Your system likely uses few-shot prompts to steer an LLM to generate news-style summaries or transitions. 4. **TTS / Narration** Generated text is converted via TTS and streamed or stitched into an audio/video feed. --- ## 🎯 Integrating RLHF into the Prompt-State To shape generation with RLHF, you need to establish a persistent **“state object”** that stores attributes useful for feedback/train loops: ### 1. Define Prompt State ```json { "tone": "neutral", "length": "medium", "focus": ["world", "tech"], "recency_bias": 0.8, "coherence_score": null } ``` - **tone, length, focus**: meta-controls for what's generated. - **recency_bias**: weight given to freshness of sources. - **coherence_score**: placeholder for future feedback. ### 2. Augment Prompt Template Your generation prompt now includes state context: ``` You are an AI news narrator. Current state: tone={{tone}}, length={{length}}, focus={{focus}}, recency_bias={{recency_bias}}. Generate a {{length}}-length update focusing on {{focus}} topics, with a {{tone}} tone… ``` ### 3. Collect Feedback - **Explicit**: User ratings (1–5 stars). - **Implicit**: Metrics like listening duration, skip rate, or coherence heuristics. Feed that back into the state, updating `coherence_score` or adjusting bias/tone values. ### 4. Train an RLHF Reward Model - Create a reward model *r(prompt, generated_text)* using human feedback. - At generation time, use reinforcement learning (e.g., PPO) to **tune** the LLM with your reward model to satisfy the stated state variables. - Alternatively, use a lightweight policy head that takes your state and yields generation parameters (max tokens, temperature, top‑p). --- ## 📋 Integration Strategy | Step | Description | |------|-------------| | 1 | Instrument generation to log `(state, text)` pairs plus feedback. | | 2 | Fine‑tune a reward model that scores generated text against desired state variables. | | 3 | Use RL (e.g. PPO) to optimize generation policy toward higher rewards. | | 4 | In your production loop, update `state` post-generation based on received feedback. | --- ## ✅ Action Plan - Decide which state vars matter most (tone, brevity, topic mix). - Modify prompt template accordingly to accept state injection. - Build a lightweight feedback interface (e.g., thumbs up/down). - Prepare dataset of `(state, generated_text, feedback_score)`. - Train reward model, then optimize generation policy. - Loop: produce → collect feedback → update state → retrain periodically. --- 🔧 This setup lets you gradually refine your infinite newsflow — making it more coherent, on-topic, and engaging over time. Would you like code snippets for stateful prompt injection or for training the reward model?