agentic-think-v2-react-hybrid

Agentic tool-calling SFT corpus, 212,396 conversations, where 36% of the episodes carry reasoning generated in a gold-blind ReAct loop instead of after-the-fact annotation. Built by ENERZAi for small-model (1.7B ternary) agentic SFT.

This is a drop-in successor to HBKenerzai/agentic-think-v1: same episodes, same scale, same schema — only the <think> text differs on the replaced rows.

Why it exists

In v1 every think block was written in annotate mode: the teacher was shown the gold action and asked to explain it. That produces reasoning which is really a post-hoc rationalization — measurably so. On rounds where the correct behavior is to call nothing, 83% of v1 action-paragraphs open with a stock verdict ("No function is called…"): the conclusion comes first and the reasoning is backfilled behind it.

v2 regenerates those episodes in react-match mode: the teacher never sees the gold action, solves the turn itself, and the episode is kept only if its function calls match gold (function name + normalized arguments, up to 5 attempts). Reasoning that survives is, by construction, reasoning that actually produced the action.

metric (no-call rounds)	v1 (annotate)	v2 (hybrid)
action paragraph opens with a stock verdict	74.0%	69.3%
— restricted to the 36% replaced rows	83.3%	17.4%
assistant rounds carrying reasoning	95.6%	95.8%

Schema

Identical to v1, plus one field.

field	type	description
id	str	<source>-<episode> unique id
source	str	one of the 9 sources below
tools	str (JSON)	OpenAI-style function catalog (distractor-augmented, 9–16 entries)
messages	str (JSON)	OpenAI-style chat messages; every assistant message carries reasoning_content alongside content and tool_calls
think_source	str	new — v24_react (gold-blind, match-filtered) or v23_annotate (v1 reasoning, unchanged)
history_mode	str, optional	present on robot rows only

from datasets import load_dataset
ds = load_dataset("HBKenerzai/agentic-think-v2-react-hybrid", split="train")
react_only = ds.filter(lambda r: r["think_source"] == "v24_react")   # 76,723 rows

Composition

file	rows	react-replaced	upstream license
nemotron_tc.jsonl	53,751	0	CC-BY-4.0
glaive.jsonl	44,980	21,844 (48.6%)	Apache-2.0
xlam.jsonl	40,000	25,271 (63.2%)	CC-BY-4.0
synth_apigen.jsonl	28,932	20,509 (70.9%)	CC-BY-4.0
nemotron_ia.jsonl	21,929	0	CC-BY-4.0
toolace.jsonl	10,322	7,627 (73.9%)	Apache-2.0
robot.jsonl	5,872	0	CC-BY-4.0
apigen_mt.jsonl	4,778	877 (18.4%)	CC-BY-4.0
hermes.jsonl	1,832	595 (32.5%)	Apache-2.0
total	212,396	76,723 (36.1%)

Known limitations

• Multi-turn episodes are not upgraded. The two nemotron splits (75,680 rows, 36% of the corpus) keep their v1 annotate reasoning. React-match yields only ~15% on long episodes, so regenerating them was not affordable; the replacement is concentrated in single- and few-turn sources.
• Two reasoning styles coexist. Annotate reasoning targets 95–200 words, react reasoning 45–120, so the corpus mixes two length distributions. Filter on think_source if you need a homogeneous set.
• Reasoning is teacher-generated (Qwen3.8-27B-FP8) and not human-verified.
• Some sources are heavily duplicated upstream — glaive in particular repeats a single user request up to 3,848 times, each paired with a different function catalog.

Method

• Teacher: Qwen3.8-27B-FP8, visible-channel thinking disabled, reasoning emitted as a three-paragraph <reasoning> block (situation / action / flow).
• React-match: gold-blind generation, per-step matching on function name plus normalized arguments, up to 5 attempts per mismatched round, recorded tool results replayed into the context.
• Rejection (no-call) episodes are augmented to 9–15 functions before generation, with a guard that drops any distractor whose name overlaps the user's request — so a refusal stays correct under the fatter catalog.

Provenance

Built on the agentic-datagen pipeline (LLM_Hard_STE, feature/agentic-datagen-vllm) by ENERZAi.