Update README.md

README.md

@@ -16,12 +16,12 @@ pipeline_tag: text-generation
 
 Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.
 
-Highlights:
 
-- **End-to-End Self-Improvement**: Ornith-1.5 extends the self-scaffolding framework introduced in Ornith-1.0 into a complete self-improvement loop: the model proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for reinforcement learning, continuously creating new learning experiences from which it can improve. Task generation, scaffold construction, and solution rollouts are all optimized jointly with GRPO, so the model continually expands its own curriculum and adapts its problem-solving strategies.
-- **State-of-the-Art General Intelligence**: Designed for strong general-purpose intelligence across reasoning, agentic, and coding tasks, Ornith-1.5 achieves state-of-the-art performance among open-source models of comparable size across a broad range of benchmarks.
-- **Edge-Deployable 9B**: Ornith-1.5-9B achieves 47.0 on Terminal-Bench 2.1 (Claude Code) and 70.6 on SWE-Bench Verified — matching or exceeding much larger models such as Gemma 4-31B and Qwen 3.6-35B. With its quantized **Ornith-1.5-9B-Mobile** version, it can be readily deployed on iPhone and Android devices.
-- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
+Ornith-1.5 extends Ornith-1.0 by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning.
+
+Each training cycle proceeds in three stages. Given an environment or codebase, high-level instructions about the task type, and access to the model's previous task-solving history, the system proposes progressively harder tasks that go beyond what the model has already solved. For each task, the model then generates or refines a task-specific scaffold — the instructions, tools, decomposition strategy, and orchestration used to approach the problem. Conditioned on the task and scaffold, the policy produces a solution rollout. Reward from the rollout is propagated across all three stages, so the system learns not only to produce better solutions, but also to generate more useful training tasks and construct more effective scaffolds.
+
+Repeated over training, this creates a closed self-improvement loop in which stronger policies enable the generation of harder and more informative tasks, evolving scaffolds discover better ways to elicit the model's capabilities, and higher-quality rollouts provide increasingly effective learning signals. For more details on the task, harness, and rollout reward design, please refer to our [blog](https://deep-reinforce.com/ornith.html).
 
 <img style="width: 100%; max-width: 900px;" src="assets/ornith_9b_eval.png" alt="Ornith 1.5 9B Benchmark Results" title="Ornith 1.5 9B Benchmark Results">
 
@@ -193,13 +193,6 @@ This model card documents **Ornith-1.5-9B**, the most lightweight member of the
 
 </div>
 
-## Self-Improvement through Self-Generated Tasks, Harnesses, and Solutions
-
-Ornith-1.5 extends Ornith-1.0 by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning.
-
-Each training cycle proceeds in three stages. Given an environment or codebase, high-level instructions about the task type, and access to the model's previous task-solving history, the system proposes progressively harder tasks that go beyond what the model has already solved. For each task, the model then generates or refines a task-specific scaffold — the instructions, tools, decomposition strategy, and orchestration used to approach the problem. Conditioned on the task and scaffold, the policy produces a solution rollout. Reward from the rollout is propagated across all three stages, so the system learns not only to produce better solutions, but also to generate more useful training tasks and construct more effective scaffolds.
-
-Repeated over training, this creates a closed self-improvement loop in which stronger policies enable the generation of harder and more informative tasks, evolving scaffolds discover better ways to elicit the model's capabilities, and higher-quality rollouts provide increasingly effective learning signals. For more details on the task, harness, and rollout reward design, please refer to our [blog](https://deep-reinforce.com/ornith.html).
 
 ## Quickstart