continualcode 0.6.0

A coding agent that learns from your corrections in real-time via on-policy self-distillation

continualcode

A coding agent that learns from your corrections in real-time. Built on Tinker.

When you deny a tool call with feedback, the model uses your correction as context to teach itself via on-policy self-distillation, takes a gradient step on LoRA, and retries with updated weights.

You: "fix the test"
Agent: write(test.py, ...)       # overwrites the file
You: n → "use edit_lines; don't overwrite"
  → SDPO update runs immediately
  → agent retries with updated weights
Agent: edit_lines(test.py, 14, 17, ...)
You: y

Install

pip install continualcode
export TINKER_API_KEY=<your-key>
continualcode

How it works

Four feedback types, one training signal. Your correction becomes privileged context for a self-teacher (same model, richer input). Per-token KL between teacher and student = dense training signal — O(N) bits per correction, not O(1). One gradient step on LoRA, retry with updated weights.

Full explanation →

Why not DPO / GRPO / PPO / SFT

DPO needs preference pairs and has no per-token credit. GRPO needs 64 samples per prompt — absurd UX for a CLI. PPO doubles memory with a critic network. SFT on corrections is off-policy and causes catastrophic forgetting. Self-distillation is the unique intersection: dense signal, on-policy, no extra models, mode-seeking stability.

Full reasoning →

Code layout

• train.py — SDPO core: teacher prompt, logprob scoring, IS update, sampler refresh
• tui.py — interactive CLI: approve/deny/edit, correction prompt, /metrics
• tools.py — tool implementations + structured feedback
• benchmarks/auto_train.py — automated training loop (LCB, multi-rollout GRPO + SDPO)
• demo/ — deny → train → retry end-to-end

References

• SDPO — Hübotter et al. 2026
• SDFT — Shenfeld, Damani, Guestrin 2026
• GKD — Agarwal et al. 2023
• Tinker — training API