Terminal-RSI

Terminal-RSI-27B is a Qwen3.8-27B-based terminal-agent model released by the Terminal-RSI project. It is intended for repository inspection, code editing, dependency repair, scientific workflow reconstruction, and other multi-step tasks that require interaction with an executable environment and verification of the resulting state.

Terminal-RSI alternates Recursive Self-Rewrite (RSR) of verified execution trajectories with critic-inheriting Recursive Policy-Refine (RPR). The framework aims to turn successful exploration into reproducible behavior and partial progress into strictly verified task completion.

• Paper: Terminal-RSI: Recursive Self-Improving Terminal Agents via Recursive Self-Rewrite and Policy-Refine
• Project: Terminal-RSI project page
• Code: jyyang26/Terminal-RSI
• Model: Teriminal-RSI/Terminal-RSI-27B

Model details

Training approach

The paper describes the retained sequence:

Base → RSR-1 → RPR-1 → RSR-2 → RPR-2

• Recursive Self-Rewrite (RSR): Discover verified trajectories across complementary harnesses, rewrite them into runbooks, screen for leakage and compatibility, and execute them afresh. Accepted public trajectories provide actor-only supervised updates; critic weights are carried through the rewrite stage.
• Recursive Policy-Refine (RPR): Recalibrate an inherited critic with the actor frozen, then jointly optimize actor and critic using PPO. The initial Warm-up Critic Model is prepared separately from the four critic-only calibration updates at the start of each refinement phase.
• Family Dense Reward (FDR): Normalize verifier-measured partial progress across task families. The paper uses FDR in RPR-1 and the ordinary RPR-2 branch.
• Success-Prioritized Reward (SPR): Combine a strict-success bonus with bounded partial-progress credit. The paper's retained Long-Reward RPR-2 branch uses SPR.
• Turn-Boundary Skip-Observation GAE (TB-GAE): Formulate credit assignment over generated tokens while skipping observation recursion nodes and applying decay across interaction-turn boundaries. This is a methodological contribution, not an independently established component-level performance gain. The paper explicitly notes that deployment of the estimator requires checkpoint/configuration evidence; its documented first phase uses standard GAE with unit discount and trace factors.

Paper-reported evaluation

These results refer to the paper's retained RPR-2 step-18 model; see the checkpoint distinction above.

Retained-stage scores are arithmetic means over three inference runs (Mean@3). The four-suite mean excludes LHTB and SWR100 and is not task-weighted. LHTB process reward and strict completion are separate metrics.

Evaluation harness

The paper reports:

• temperature 0.60, top-p 0.95;
• up to 8,192 generated tokens per turn and 96,000 input tokens per call;
• structured JSON actions and explicit thinking mode disabled;
• linear interaction history with proactive summarization triggered at 32,000 accumulated tokens;
• benchmark-specific termination and time limits, with a configured upper bound of 100,000 turns.

Performance depends on the agent harness, tools, prompts, time budget, and verifier. A standalone chat example does not reproduce these agent evaluations. External frontier-model comparisons are omitted here because the paper notes incomplete primary-source/protocol reconciliation.

Loading the model

Use a Transformers version that supports Qwen3_5ForConditionalGeneration, plus PyTorch, Accelerate, and Safetensors. The packaged configuration records the export version as 5.8.0.dev0; configuration loading was checked locally with Transformers 5.17.0. Full model loading and GPU inference were not tested as part of packaging.

import torch
from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration

model_id = "Teriminal-RSI/Terminal-RSI-27B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a terminal assistant. Explain your next action; do not claim to have run commands."},
    {"role": "user", "content": "How would you investigate a Python project whose tests fail because a dependency is missing?"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        do_sample=True,