~900 words. Structure: TL;DR with headline number, problem statement
(reasoning gap), env architecture, multi-agent delegation, training
recipe (Unsloth + TRL GRPO), real results (reward curve + bar chart),
reward-hacking evidence (eval harness disclosure), why it matters
(market context), reproduction steps, links.
Designed to paste directly into https://huggingface.co/blog/new with
markdown front-matter. All images reference the live HF Space's plots
folder so they render even after the user publishes.
README:
- Reframe Results section: deterministic + Gemini baselines first (reproducible by anyone),
GRPO numbers honestly labeled as 'recipe targets' rather than completed runs
- Add 'Reproduce These Numbers' section with real CLI commands (no fake flags)
- Lead with the 'reasoning gap' as the headline finding
inference.py:
- Shorter, example-driven system prompt (was 30 lines of CAPS rules; now 3 worked
examples of tool-output -> action) so small open models like Llama 3.2 3B / Qwen 1.5B
actually call use_tool/submit_finding instead of looping on list_tools
- Default sampling TEMPERATURE 0.1 -> 0.5 (override via INFERENCE_TEMPERATURE env)
- Document new env var in .env.example
Brand consistency:
- generate_plots.py + aisha_rl_training.py: replace hardcoded anshumanatrey HF Space URL
with Sayuj63/Vapt-env, strip remaining AISHA brand from plot titles and module docstrings
- Filenames kept (referenced by ~15 doc lines) — only user-visible strings cleaned
docs/HACKATHON/: drop…
