CopyleftCultivars/Agriculture-Agent-RL-Training-Data

Agriculture Agent RL Training Data

A growing dataset of RL rollout trajectories for LLM agents on natural/regenerative farming — the first RL/trajectory-shaped dataset in the Copyleft Cultivars collection (every prior dataset here is SFT/conversational Q&A). Agents call real tools (primarily cultivars-mcp, a plant-genomics MCP server) across 9 knowledge categories (plus a 10th, organic_chemistry_soil_science, added 2026-08-11) and are scored by a multi-component reward (tool-use correctness, LLM-judged content quality, a structural citation requirement for indigenous-knowledge content, and an anti-essentializing check).

This dataset grows additively. Each generation-pipeline run adds new files under data/train/ and data/val/ — nothing is ever overwritten — so load_dataset("CopyleftCultivars/Agriculture-Agent-RL-Training-Data") picks up every run to date automatically.

Intended use

Primary use — RL fine-tuning. The record shape (see below) matches verifiers' native rollout convention with no reformatting needed, so it's directly loadable into a TRL GRPOTrainer or any verifiers-compatible RL loop. This is meant to complement, not replace, the org's existing SFT datasets (knf-sharegpt, qwen3-natural-farming-v5clean-sft, Semisynthetic_Data_Natural_Farming_Fundamentals, etc.) — a natural next step is a GRPO pass on top of an SFT-trained checkpoint, using this data.

Secondary use — qualitative review. Each record is a full transcript (system/user/assistant/tool turns, not just a flattened Q&A pair), so it's also useful for eyeballing how an agent reasons and uses tools across the knowledge categories — e.g. whether it asks clarifying questions before prescribing a fix (farmer_dialogue-shaped records), or how it grounds a genetics claim in real tool output (advice_tool-shaped records).

Not intended for:

• Evaluation/benchmarking. Every record is contamination-checked against the sibling nf-benchmark-eval repo's MCQ benchmark at export time (see info.contamination_audit) specifically so this stays a clean training set — don't also use it as an eval set, that would defeat the point of the check.
• Citing indigenous-knowledge claims as community-validated. Every such record's info.ik_provenance.review_status defaults to "unreviewed" — literature-grounded, not community-consulted. See Ethics section below before using this content in anything user-facing.
• Treating the reward field as a calibrated quality score across sources. See "Known limitations" below — reward is NOT comparable across generation sources as-is.

Domains, topics, and tools

10 categories (task field), each assigned one of 3 interaction shapes — advice_tool (agent must call a real tool before answering, grounded in what it returns), farmer_dialogue (tool-optional, rewards asking clarifying questions before prescribing a fix), or scenario_critique (tool-free critique of a proposed intervention on ecological and ethical grounds). Tools are real, live calls against cultivars-mcp (plant genomics) or PubChem — not simulated.

Category	Shape	Tools
agroecology	advice_tool	find_trait_genes, list_trait_categories, lookup_gene
regenerative_agriculture	advice_tool	find_trait_genes, list_trait_categories, translate_trait_to_species
organic_farming	advice_tool	find_trait_genes, list_trait_categories, lookup_gene, list_maize_nam_founders
organic_chemistry_soil_science	advice_tool	lookup_compound (PubChem)
soil_science_and_microbiology	farmer_dialogue	none
integrated_farming_systems	farmer_dialogue	none
natural_fertilizers	scenario_critique	none
sustainable_water_management	scenario_critique	none
permaculture	scenario_critique	none
indigenous_and_traditional_knowledge	mixed — see sub-topics below	mixed

indigenous_and_traditional_knowledge has 10 sub-topics (info.topic), each with its own shape/tools rather than one category-wide setting:

Sub-topic	Shape	Tools
ethnobotany_plant_knowledge	advice_tool	find_trait_genes, list_trait_categories
traditional_seed_saving	advice_tool	resolve_accession, list_orphan_crop_requests
biocultural_diversity	advice_tool	find_trait_genes, translate_trait_to_species, list_orphan_crop_requests
traditional_cropping_systems	advice_tool	find_trait_genes, list_maize_nam_founders
traditional_ecological_knowledge	advice_tool	find_trait_genes, list_orphan_crop_requests
agroforestry_traditional	advice_tool	find_trait_genes, translate_trait_to_species
indigenous_land_management	farmer_dialogue	none
indigenous_soil_practices	farmer_dialogue	none
food_sovereignty	scenario_critique	none
community_based_resource_management	scenario_critique	none

Every topic in this category forces needs_community_review: true and a non-empty citation requirement regardless of shape — see Ethics section below.

Tool glossary (all from cultivars_bridge.py unless noted; each is a real live/curated-data call, not a stub):

• list_trait_categories() — lists the curated natural-farming-relevant trait categories (no network call)
• find_trait_genes(trait, target_species?) — canonical genes for a plant trait, from the curated trait atlas (no network call)
• lookup_gene(gene, species?, expand?) — looks up a specific gene by symbol/ID (live Ensembl Plants REST API call)
• translate_trait_to_species(trait, target_species, max_genes?) — maps a trait's known genes onto orthologs in a target species (live Ensembl Plants ortholog queries)
• resolve_accession(query, crop_type?, region?, limit?) — resolves a folk/informal seed name to a formal GRIN-Global accession (live USDA GRIN-Global API call)
• list_orphan_crop_requests() — lists community-submitted requests for under-studied ("orphan") crop trait research (no network call)
• list_maize_nam_founders(subpopulation?) — lists the 26 maize NAM (Nested Association Mapping) founder lines, a static curated panel (no network call)
• lookup_compound(name) (PubChem, pubchem_bridge.py) — molecular formula/weight/SMILES/IUPAC name for a named chemical compound, e.g. "urea" → CH4N2O, 60.06 g/mol (live PubChem PUG REST API call)

Current coverage (snapshot, will grow — not auto-updated; refresh manually)

Four generation sources have contributed so far — see "Known limitations" below for an important caveat on comparing rewards across them:

lfm2.5 (local, 5B, GPU-accelerated via Ollama): local lfm2.5, judged by the same model (self-judging). Generator agent: hermes (an internal pipeline labeling quirk — info.generation_meta.policy_model is the reliable field to filter on, not generator_agent, for distinguishing this from Hermes CLI). Covers all 9 original categories plus all 10 indigenous-knowledge sub-topics.

Hermes CLI (nous:tencent/hy3:free, cloud-backed, free tier): same model via the Nous Portal, self-judging. Generator agent: hermes_cli. Covers the same full category/topic set as lfm2.5, plus organic_chemistry_soil_science.

Antigravity/agy (Gemini 3.5 Flash (Medium)): same model via the agy CLI, self-judging. Generator agent: agy. Scoped to tool-free shapes only (farmer_dialogue, scenario_critique) — see docs/ARCHITECTURE.md for the safety rationale (agy's CLI has no verified per-tool restriction mechanism, unlike Hermes's -t flag).

qwen3.6 (local, 35B-A3B MoE, added 2026-08-11): local Qwen3.6-35B-A3B via a dedicated llama.cpp server with expert-offload, judged by the same model (self-judging). A genuine reasoning model, architecturally distinct from the other three (thinking traces, MoE routing) — added specifically so the dataset isn't limited to non-reasoning local models. Slow (~5 min/rollout on this hardware), so it's a trickle source, not bulk volume.

Every indigenous-knowledge record passed the citation gate — every claim either traces to one of the Crossref-verified sources in the source repo's CITATIONS.md, or is grounded by a successful tool call, or doesn't assert anything about indigenous/tribal knowledge specifically (the gate only blocks genuine IK assertions, not mere topic-category membership or localized/place-specific content). For an authoritative, per-run record count at any point in time, sum the kept field across every file under manifests/ in this repo.

A note on splits: every record physically loads into one HF train split (a two-split design broke load_dataset() outright whenever one side was temporarily empty — a real, legitimate outcome given the citation/ contamination gates above). Each record's info.local_split field ("train"/"val") preserves the pipeline's original intent — filter on it if you want the distinction: ds.filter(lambda x: x["info"]["local_split"] == "val").

A note on model diversity: this dataset contains trajectories from four different policy models (lfm2.5, nous:tencent/hy3:free, Gemini 3.5 Flash via Antigravity, and qwen3.6). Each model's rollouts are judged by that same model (self-judging) — reward signal is model-consistent within each source but not comparable across sources. The info.generation_meta field identifies which model generated (and judged) each record.

Known limitations — read before using reward as a quality signal

Resolved 2026-08-12 — tool-call format was inconsistent across sources. Hermes CLI's tool_calls didn't match the plain OpenAI shape every other source used: function.name was literally the string "tool_call", with the real tool name/args nested one level deeper as a JSON string and an mcp__<server>__ prefix on the real name. This was the majority shape among tool-using records (1,398/1,881, ~74%), not a minority quirk, and mattered concretely for any consumer needing one consistent serialization (e.g. an on-device executor reproducing exact tool-call output). Fixed at export time (export.py's _unwrap_hermes_tool_call) — self-heals on every publish, no data loss, raw rollout files untouched. If you loaded this dataset before 2026-08-12, re-download; info.generation_meta.policy_model still tells you which source produced each record.

Self-judging produces an inflated, non-comparable reward for the Antigravity/agy source specifically — confirmed by an independent rejudge, not just a same-source audit. A same-source audit (2026-08-11) found reward == 1.0 (the maximum) for 99.6% of agy-sourced records on tool-free shapes, versus 57-62% for the other three sources on the exact same shapes, all of which also self-judge. To test whether that reflected genuinely uniform quality or self-preference bias, a statistically significant random sample (n=100, 94 successfully scored) of agy's records was independently rejudged using a different backend/model (Hermes CLI/Nous Portal, tencent/hy3:free) against the identical judge prompt and rubric — routed through the same infrastructure as this dataset's own Hermes-CLI generation, per this project's standing "no Claude-credits for judging" policy. Result: the independent judge agreed with agy's perfect score on only 59.6% of the sample (mean 4.06/5, vs agy's self-judged mean of 5.00/5) — real, sizeable disagreement, not statistical noise (distribution: 56 fives, 32 threes, 6 ones).

Critically, the disagreement is not spread evenly — it concentrates almost entirely in indigenous/traditional-knowledge content. Broken down by category, the independent judge's mean score was 3.22/5 for indigenous_and_traditional_knowledge (n=36) and 3.67/5 for integrated_farming_systems/soil_science_and_microbiology (n=9 each), but a perfect 5.00/5 — full agreement with agy's self-judge — for every single sampled record in natural_fertilizers, permaculture, and sustainable_water_management (n=20/9/11). Spot-checking the disagreeing IK-category records found generic, well-organized farming-advice answers (not obviously essentializing or low-effort) — the gap looks less like "agy wrote bad IK content" and more like the same self-preference bias being worse specifically on the ethically-loaded rubric (the IK rubric's red-flag/essentializing checks give a judge more surface area to be either lenient or strict on, versus a purely technical rubric where both judges converge). Either way, it means the reward signal is least trustworthy exactly where this project's ethics commitments matter most.

Practical implication: don't treat reward as calibrated across info.generation_meta.policy_model values, and be especially skeptical of reward == 1.0 on agy-sourced indigenous_and_traditional_knowledge records specifically. An RL policy trained naively against this reward would over-index on agy-style responses (easy 1.0s) rather than learning a genuine quality signal for roughly a third of the dataset, and would learn the LEAST reliable signal on IK content. If you need a comparable signal across sources, re-judge with a single external judge model rather than trusting the stored reward field as-is (see scripts/rejudge_agy_sample.py for the exact method used here), or filter/reweight by source and category.

Record shape

{"prompt": [...], "completion": [...], "reward": float, "task": category, "info": {...}} — matches the verifiers library's native rollout convention, directly consumable by vf.load_environment / a TRL GRPOTrainer. info carries ik_provenance (citation/review-status metadata for indigenous-knowledge content), contamination_audit (cross-checked against the sibling nf-benchmark-eval benchmark set), generation_meta (policy/judge/seed model identifiers), and local_split (see above).

Ethics & sourcing — read before using indigenous-knowledge content

This dataset does not claim direct indigenous-community consultation. Indigenous/traditional-knowledge content is literature-grounded only, and every record that genuinely asserts something about indigenous/tribal knowledge is required to carry a real, Crossref-verified citation (or be grounded by a successful tool call) — records that fail this are excluded at export time, not just flagged. See the source repo's ETHICS.md, docs/IK_SOURCING_POLICY.md, and CITATIONS.md for the full policy and bibliography, including the redress/retraction process if a source or community identifies a problem with an entry.

License

Hippocratic License 3.0, CL-ECO-EXTR-MIL — chosen specifically for its clause protecting indigenous peoples' land and traditional knowledge from unconsented use (§3.1.10), on top of the standard Hippocratic License ethical-use conditions.

Source

Generated by the pipeline in SolshineCode/Agriculture-Agent-RL-Training-Data.

Generation paths (four distinct actor+judge combinations, all self-judging — see Known limitations above):

• lfm2.5: local lfm2.5 via Ollama, judged by the same model — see run_job() in src/agrirl/pipeline/run_generation.py
• Hermes CLI: hermes chat --provider nous -m tencent/hy3:free for policy, Nous proxy for judging — see run_job_via_hermes()
• Antigravity/agy: agy -p ... --model "Gemini 3.5 Flash (Medium)" for both policy and judging, tool-free shapes only — see run_job_via_agy() and src/agrirl/pipeline/agy_policy.py
• qwen3.6: local Qwen3.6-35B-A3B via a dedicated llama.cpp server (GPU1, expert-offload), judged by the same model — see run_qwen_trickle.py and run_job()'s ollama_base_url/file_label params

All paths share the same seed-generation step (Claude Code via generate_*_seeds.py), the same reward rubric, and the same contamination filter. The info.generation_meta block in each record identifies which path produced it.

For Copyleft Cultivars contributors

• Where this fits in the ecosystem: cultivars-mcp supplies the plant-genomics tool calls; nf-benchmark-eval (private) supplies both the category taxonomy this dataset's task field follows and the contamination-check target; qwen3-finetune (private) is the natural downstream consumer for an eventual GRPO training run; the indigenous-knowledge rubrics in qwen3-finetune's mcq_generation/quality/rubrics/ are reused directly for this dataset's reward scoring (vendored with attribution in the source repo).
• Known limitations to keep in mind before relying on this data: see the dedicated section above — reward is not comparable across sources (self-judging, and agy's judge in particular looks lenient); real-volume generation is ongoing. The info.generation_meta block identifies the model used for each record.
• Adding more data: run the generation pipeline described in the source repo's docs/ARCHITECTURE.md — it publishes here additively, no direct edits to this dataset needed. CONTRIBUTING.md there covers adding a new category spec, sourcing more indigenous-knowledge citations, or tuning a reward component.
• Questions or issues (including anything ethics/sourcing-related, e.g. a source or community flagging a problem with an indigenous-knowledge entry — see ETHICS.md's redress process): open an issue on the source repo.