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Multi-step GRPO, milestone reward, benchmark, Neo4j export, graph viz
Krishna +1: Claude Sonnet 4.6Training: - Multi-step outcome GRPO: model generates 3-5 action plans; full plan executed in env; terminal reward is the training signal (10x more reward variance than single-step) - Async-parallel reward: asyncio.gather runs all G=8 plans concurrently - N_SEEDS_PER_TASK 20->40 for more case diversity (120 total prompts) - max_completion_length 600->800 to fit 4-5 JSON action blocks Environment: - Milestone reward (+0.10) on first visit to causal gene node; creates phenotype->gene->variant two-stage reward landscape Server: - /graph/subgraph endpoint returns D3.js-ready subgraph JSON New files: - benchmark.py: multi-model comparison (Qwen3-0.6/1.7/4B, Llama-3.2-3B, Phi-3.5-mini) via HF Inference Router - scripts/export_neo4j.py: export full graph to Cypher for Neo4j Aura Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>2a62ebe -
upgrades for openenv validation
b654948 -
Add phenotypes_absent obs field, fix training config, update README framing
Krishna +1: Claude Sonnet 4.6- NaradaObservation gains phenotypes_absent + phenotype_absent_names fields; all three case generators populate them from disease HPO terms not assigned to this patient, giving the model explicit diagnostic exclusion signal - environment.py passes absent phenotypes through _build_observation - inference.py and notebook format_obs both surface absent phenotypes in prompt - Notebook Cell 7: max_completion_length 300 → 600 (fixes null-training root cause where Qwen3 thinking tokens consumed all 300 budget, reward_std=0); auto-clears unsloth_compiled_cache on run - Notebook Cell 4: system prompt adds rule 5 for absent phenotypes - README: adds HF Space link, scopes Narada to variant-prioritization substage, clarifies Adversary as planned future work, adds multi-model benchmark table Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>6c965a0
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Add training notebook, architecture docs, baseline benchmark, and WS fixes
Krishna +1: Claude Sonnet 4.6- training/narada_grpo.ipynb: Colab notebook for GRPO fine-tuning Qwen3-1.7B with curriculum training (monogenic → oligogenic → phenotype_mismatch) - ARCHITECTURE.md: full docs — graph schema, reward design, session lifecycle, three-agent system, action/observation space - README: baseline benchmark table (llama-3.3-70b-versatile, zero-shot); shows high variance motivating GRPO training - inference.py: auto-detect Groq vs HF backend from env vars (GROQ_API_KEY or HF_TOKEN); handle ConnectionClosed cleanly instead of silent reconnect - client.py: tighter ping settings (15s interval), remove silent reconnect that masked session loss after WS dropout - pyproject.toml / requirements.txt: add python-dotenv dependency - .gitignore: add .env to secrets exclusions Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>feeb6b0 -
Add OpenEnv standard endpoints and uv.lock
Krishna +1: Claude Sonnet 4.6- GET /metadata: name + description for validator - GET /schema: Pydantic JSON schemas for action/observation/state - POST /mcp: JSON-RPC 2.0 endpoint for MCP tool discovery - requirements.txt + pyproject.toml: add openenv-core>=0.2.0 - pyproject.toml: [project.scripts] server = narada.server.app:main - uv.lock: generated lockfile Local validation now passes. Live validator passes 3/6 → 6/6. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>b3577f1 -
Rename project to Narada
Krishna +1: Claude Sonnet 4.6Rename Python package clindetect → narada throughout: - src/envs/narada/ replaces src/envs/clindetect/ - Class names: ClinDetectGraph → NaradaGraph, ClinDetectEnvironment → NaradaEnvironment, ClinDetectEnv → NaradaEnv, ClinDetectAction/Observation/State → Narada* - All entry points, uvicorn module paths, openenv.yaml, Dockerfile CMD updated - README, pyproject.toml, inference.py ENV_URL updated to narada-env space Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>9e4dc47 -
Initial ClinDetect OpenEnv environment
Krishna +1: Claude Sonnet 4.6Gene-disease knowledge graph RL environment for rare disease diagnosis. 74K nodes (HPO phenotypes, diseases, genes, variants) built from real ClinVar + HPO data. Three task tiers: monogenic, oligogenic, phenotype_mismatch. Server, client, inference.py, openenv.yaml, Dockerfile all validated locally. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>077e61e