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AI / Reinforcement learning / Training environment

EETRE

From classifying mail to deciding what happens next.

Train an agent to reply, escalate, archive, or mark spam—not just label an email.

My contribution
Built the email-task environment, action grading, training workflow, and integrations for live execution.
Result
Archived training runs and recorded examples of replies and Slack escalation.

FROM THE PROJECT

Archived training results

Archived EETRE training plots, including SFT loss, GRPO reward, and per-difficulty comparison.
Original repository plots. These are archived experimental results; the individual panels should not be treated as one uniform evaluation. Source ↗
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available email actions
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reasoning / decision / audit stages
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simulated and live modes

THE CHALLENGE

An inbox needs actions, not only labels

A busy inbox mixes incidents, support requests, newsletters, and phishing. Choosing a label does not resolve that workload. EETRE asks whether an agent can choose and carry out the next useful action: reply, escalate, archive, or mark spam.

ENGINEERING

Grading the next action in a workflow

The environment exposes reset, state, and step operations. An observation includes the task objective, difficulty, inbox, processed message IDs, and step budget. Each action updates the episode history and produces a structured reward and completion state.

The grader considers action correctness, response content, and efficiency. It penalizes unsafe replies to spam, repeated work, missed replies, unmet task dependencies, and repeated-action collapse. Optional reward noise is configurable, with a seed for reproducible noise.

The live-agent workflow separates reasoning, decision, and audit stages. Configured integrations can read and act on a mailbox through IMAP/SMTP and escalate to Slack. The repository also contains a simpler rule-based orchestrator; its behavior should not be confused with learned-model performance.

Grade the workflow, not just the label

Episode state tracks processed emails, action history, dependencies, and remaining steps. Response quality uses explicit keyword and length checks rather than a claim of comprehensive semantic evaluation.

Keep training and execution boundaries visible

The simulated curriculum supplies bounded tasks; live-mail integration is a separate configured path. GRPO training uses the environment API.

Inspect the evidence by run

Training plots, response examples, and escalation screenshots are archived in the repository. They document experiments rather than guarantee performance on every inbox.

ARCHITECTURE

From email task to training feedback

EETRE architectureSimulated email tasks or a configured live mailbox feed the reasoning, decision, and audit stages. Approved actions execute in the relevant environment; the simulated task grader returns feedback for training.Simulated curriculumObjective + step budgetConfigured live mailboxIMAP / SMTPReasoning → decisionIntent + proposed actionAuditorReview unsafe choicesApproved actionReply / escalate / archive / spamSimulated task graderReward → training feedback

Simulated email tasks or a configured live mailbox feed the reasoning, decision, and audit stages. Approved actions execute in the relevant environment; the simulated task grader returns feedback for training.

RESULTS AND LIMITS

Training artifacts and a recorded end-to-end workflow

The repository includes the training script, notebook, archived SFT/GRPO plots, a short demo, and screenshots of live replies and Slack escalation. Different plots summarize different runs; this portfolio does not merge them into a new benchmark claim.

Current boundaries

Live mode can send messages or change mailbox state and requires explicitly configured credentials. Its response grader is heuristic; the reported experiments are not a general production-safety evaluation.

EXPLORE THE SOURCE

Inspect tasks, rewards, and training runs

Repository review: 14 September 2026. Recorded test results retain their original scope; they were not rerun for this site.

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