## Observations on Length Bias Its known that complex problems tend to bias responses in LLM training with RL for longer responses. Makes sense, the harder the issue, the Expected reward of the output will be higher, the longer its CoT. As its likelier to "reason" correctly. This can be seen in many other problems, like in the Dr. GRPO paper. For RLMs, this seems to also happen with tools usage. Training examples seem to follow the following trend in answers length. 1) starts with a value close to max, 2) then lowers as its learning the "easy" parts of the problem, before 3) length starts increasing cause now we're on the harder part of the problem. Examples are shown with two different training runs (with batch size 128 and 256). The 128 batch shows apparent stability in the overall reward, but individually, the formation energy metric has big variance at the smaller batch size. The 256 batch size did well enough! even after 100 steps, it felt like it could keep advancing. An interesting observation, is that the sequence length gets larger at the end, even larger than the base model. Showing that the CoT increases a ton at the end. something to consider for evolving curriculum. You rather soften length targets for difficult regions in learning. How would you define difficult stages? Well, the ones that take longer! These are heuristics. Behavioural Examples of LLMs!: Step 0: Bevaviour 1: Too many exploratory steps: (Look for skills, cif, over and over) Many steps before giving an answer Behaviour 2: Writing buggy python code - Code that expects the cif file, but the input to the function is the file path - code with syntax errors """ Cell In[6], line 85 ]) ^ SyntaxError: closing parenthesis ')' does not match opening parenthesis '[' on line 78 """ """ --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[5], line 1 ----> 1 from pymatgen import Structure 2 import json 3 4 # Read the CIF and create a structure ImportError: cannot import name 'Structure' from 'pymatgen' (unknown location)""" """ --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[6], line 2 1 from pymatgen.core import Structure ----> 2 from pymatgen.io.cif import CifReader 3 from pymatgen.analysis.distance import Distances 4 5 # Read the CIF and create a structure ImportError: cannot import name 'CifReader' from 'pymatgen.io.cif' (/root/.local/share/uv/tools/rlm/lib/python3.11/site-packages/pymatgen/io/cif.py)""" """# Fix out-of-bound fractional coordinates and create a proposed CIF def wrap_coord(x, to=0): """Wrap fractional coordinate to [0, to).""" return x % to # Process all atoms, wrapping coordinates fixed_atoms = [] for sym, label, mult, x, y, z, occ in atoms: x_wrapped = wrap_coord(x) y_wrapped = wrap_coord(y) z_wrapped = wrap_coord(z) fixed_atoms.append((sym, label, mult, x_wrapped, y_wrapped, z_wrapped, occ)) print(f"{label}: ({x:.6f}, {y:.6f}, {z:.6f}) -> ({x_wrapped:.6f}, {y_wrapped:.6f}, {z_wrapped:.6f})") print(f"\nFixed total atoms: {len(fixed_atoms)}")""" Behaviour 3: - Using programatic cif generation Behaviour 4: - Trying to use pymatgen from python unsuccesfully (the imports are not correct) Behaviour 5: - Using python code with no print statements (the llm doesnt have any signal that it wrote correctly) Failure Example: """ { "code": "import numpy as np\n\n# Parse the CIF and compute distances\ndata = {\n 'a': 3.33277000,\n 'b': 3.78041000,\n 'c': 10.06009000,\n 'alpha': 90.0,\n 'beta': 90.0,\n 'gamma': 90.0\n}\n\nangles = {k: np.radians(v) for k, v in data.items() if k == 'alpha' or k == 'beta' or k == 'gamma'}\nangles['alpha'] = grades['alpha'] = np.radians(90.0)\nangles['beta'] = np.radians(90.0)\nangles['gamma'] = np.radians(90.0)\n\natoms = [\n ('V0', 'V', np.array([0.01805012, -0.01891080, 0.36268730])),\n ('V1', 'V', np.array([0.26416577, 0.95004073, 0.85285202])),\n ('V2', 'V', np.array([0.77058920, 0.74371467, 0.56602285])),\n ('V3', 'V', np.array([0.83094858, 0.54759343, 0.10933162])),\n ('O4', 'O', np.array([0.16686101, 0.67389714, 0.43096165])),\n ('O5', 'O', np.array([0.31782828, 0.70917813, 0.99000810])),\n ('O6', 'O', np.array([0.76417379, 0.85567672, 0.43284224])),\n ('O7', 'O', np.array([1.20667685, 0.09962754, 0.01154999])),\n ('F8', 'F', np.array([0.32151478, 0.50781390, 0.75968459])),\n ('F9', 'F', np.array([-0.00243231, 0.77765567, 0.27918440])),\n ('F10', 'F', np.array([0.92612140, 0.25516086, 0.30141539])),\n ('F11', 'F', np.array([0.54558987, 0.68064575, 0.69152123])),\n]\n\n# Wrap coordinates to [0, 1)\nfor name, sym, pos in atoms:\n pos = np.array([(p % 1.0) for p in pos])\n atoms.append((name, sym, pos))\n\ndef frac_to_cart(frac, a, b, c, alpha, beta, gamma):\n \"\"\"Convert fractional to Cartesian coordinates for triclinic cell\"\"\"\n a, b, c = a, b, c\n alpha, beta, gamma = alpha, beta, gamma\n \n # For orthorhombic-like cell (alpha=beta=gamma=90)\n return np.array([frac[0]*a, frac[1]*b, frac[2]*c])\n\ndef cart_distance(p1, p2):\n return np.sqrt(np.sum((p1 - p2)**2))\n\n# Compute all interatomic distances\nprint(\"Interatomic distances:\")\ndistance_list = []\nfor i in range(len(atoms)):\n for j in range(i+1, len(atoms)):\n name1, sym1, pos1 = atoms[i]\n name2, sym2, pos2 = atoms[j]\n if sym1 == sym2:\n dist = cart_distance(np.array([a*pos1[0], b*pos1[1], c*pos1[2]]),\n np.array([a*pos2[0], b*pos2[1], c*pos2[2]]))\n if dist < 3.0: # Only show short distances\n print(f\"{name1}-{name2}: {dist:.4f} Å (same element)\")\n else:\n dist = cart_distance(np.array([a*pos1[0], b*pos1[1], c*pos1[2]]),\n np.array([a*pos2[0], b*pos2[1], c*pos2[2]]))\n if dist < 3.0:\n print(f\"{name1}-{name2}: {dist:.4f} Å\")" } """ --> """ NameError: name 'grades' is not defined """ Getting distances examples: """ { "code": "import numpy as np\n\na, b, c = 3.33277000, 3.78041000, 10.06009000\n\natoms = [\n ('V0', 'V', np.array([0.01805012, -0.01891080, 0.36268730])),\n ('V1', 'V', np.array([0.26416577, 0.95004073, 0.85285202])),\n ('V2', 'V', np.array([0.77058920, 0.74371467, 0.56602285])),\n ('V3', 'V', np.array([0.83094858, 0.54759343, 0.10933162])),\n ('O4', 'O', np.array([0.16686101, 0.67389714, 0.43096165])),\n ('O5', 'O', np.array([0.31782828, 0.70917813, 0.99000810])),\n ('O6', 'O', np.array([0.76417379, 0.85567672, 0.43284224])),\n ('O7', 'O', np.array([1.20667685, 0.09962754, 0.01154999])),\n ('F8', 'F', np.array([0.32151478, 0.50781390, 0.75968459])),\n ('F9', 'F', np.array([-0.00243231, 0.77765567, 0.27918440])),\n ('F10', 'F', np.array([0.92612140, 0.25516086, 0.30141539])),\n ('F11', 'F', np.array([0.54558987, 0.68064575, 0.69152123])),\n]\n\n# Wrap coordinates to [0, 1)\nfor name, sym, pos in atoms:\n pos = np.array([(p % 1.0) for p in pos])\n\ndef frac_to_cart(frac):\n return np.array([frac[0]*a, frac[1]*b, frac[2]*c])\n\ndef get_coord(atoms, name):\n return atoms[name]\n\n# Compute all interatomic distances\nprint(\"Interatomic distances:\")\nshort_distances = []\nall_distances = []\nfor i in range(len(atoms)):\n name1, sym1, pos1 = atoms[i]\n for j in range(i+1, len(atoms)):\n name2, sym2, pos2 = atoms[j]\n pos1_cart = frac_to_cart(pos1)\n pos2_cart = frac_to_cart(pos2)\n dist = np.linalg.norm(pos1_cart - pos2_cart)\n all_distances.append((name1, sym1, name2, sym2, dist))\n if dist < 3.0:\n print(f\"{name1}({sym1})-{name2}({sym2}): {dist:.4f} Å\")\n short_distances.append((name1, sym1, name2, sym2, dist))\n\n# Also find very short distances that might be < V-V contact (check for overlaps)\nprint(\"\\n\\nVery short distances (< 2.0 Å):\")\nfor name1, sym1, name2, sym2, dist in all_distances:\n if dist < 2.0:\n print(f\"{name1}({sym1})-{name2}({sym2}): {dist:.4f} Å (POSSIBLE OVERLAP)\")" } """ Output: """ Interatomic distances: V0(V)-O4(O): 2.7527 Å V1(V)-O5(O): 1.6628 Å V1(V)-F8(F): 1.9261 Å V1(V)-F11(F): 2.1333 Å V2(V)-O4(O): 2.4422 Å V2(V)-O6(O): 1.4052 Å V2(V)-F8(F): 2.6136 Å V2(V)-F11(F): 1.4877 Å V3(V)-O7(O): 2.3246 Å V3(V)-F10(F): 2.2487 Å O4(O)-O6(O): 2.1061 Å O4(O)-F9(F): 1.6744 Å O4(O)-F11(F): 2.9094 Å O5(O)-F8(F): 2.4389 Å O6(O)-F10(F): 2.6820 Å O6(O)-F11(F): 2.7822 Å F8(F)-F11(F): 1.2062 Å Very short distances (< 2.0 Å): V1(V)-O5(O): 1.6628 Å (POSSIBLE OVERLAP) V1(V)-F8(F): 1.9261 Å (POSSIBLE OVERLAP) V2(V)-O6(O): 1.4052 Å (POSSIBLE OVERLAP) V2(V)-F11(F): 1.4877 Å (POSSIBLE OVERLAP) O4(O)-F9(F): 1.6744 Å (POSSIBLE OVERLAP) F8(F)-F11(F): 1.2062 Å (POSSIBLE OVERLAP) """ example: code: """ { "code": "# Create a reasonable relaxed VOF structure\n# Since space group is P1 and lattice is triclinic-like, we can place atoms at reasonable positions\n\nnew_cif_content = '''# generated using pymatgen\ndata_VOF\n_symmetry_space_group_name_H-M 'P 1'\n_cell_length_a 3.33277000\n_cell_length_b 3.78041000\n_cell_length_c 10.06009000\n_cell_angle_alpha 90.00000000\n_cell_angle_beta 90.00000000\n_cell_angle_gamma 90.00000000\n_symmetry_Int_Tables_number 1\n_chemical_formula_structural VOF\n_chemical_formula_sum 'V4 O4 F4'\n_cell_volume 126.74945851\n_cell_formula_units_Z 4\nloop_\n _symmetry_equiv_pos_site_id\n _symmetry_equiv_pos_as_xyz\n 1 'x, y, z'\nloop_\n _atom_site_type_symbol\n _atom_site_label\n _atom_site_symmetry_multiplicity\n _atom_site_fract_x\n _atom_site_fract_y\n _atom_site_fract_z\n _atom_site_occupancy\n'''\n\n# Define relaxed positions to avoid overlaps\n# Key is to arrange atoms at ~2.0 Å bond distances from V atoms\n# V atoms as tetrahedral/ distorted octahedral coordination centers\n\n# Place V atoms in a reasonable framework\n# Try placing them close but not overlapping\nv_positions = [\n (0.0, 0.0, 0.0), # V0 at origin\n (0.5, 0.0, 0.0), # V1 \n (0.0, 0.5, 0.0), # V2\n (0.0, 0.0, 0.5), # V3 at different z\n\n # O atoms placed around V atoms at ~1.95 Å\n (0.19, 0.0, 0.0), # O1 near V0\n (0.40, 0.0, 0.19), # O2 near some V\n (0.0, 0.19, 0.0), # O3 near V2\n (0.0, 0.0, 0.31), # O4 near V3\n\n # F atoms placed to avoid F-F overlap and bond at reasonable distances\n (0.0, 0.19, 0.19), # F1 near V0\n (0.30, 0.0, 0.0), # F2 \n (0.0, 0.0, 0.30), # F3\n (0.31, 0.0, 0.19), # F4\n]\n\n# Let me think more carefully about this. With P1 and orthorhombic angles\n# I need to ensure atoms don't overlap and have reasonable bond lengths.\n\n# Let me try a more systematic approach - put V atoms at high symmetry positions\n# and O, F around them\n\nbetter_v_positions = [\n (0.0, 0.0, 0.0),\n (0.25, 0.0, 0.0),\n (0.0, 0.25, 0.0),\n (0.25, 0.25, 0.25)\n]\n\n# Calculate positions for O and F to be at ~1.9-2.0 Å from their nearest V\ndef calc_dist(frac1, frac2, a, b, c):\n p1 = np.array([frac1[0]*a, frac1[1]*b, frac1[2]*c])\n p2 = np.array([frac2[0]*a, frac2[1]*b, frac2[2]*c])\n return np.linalg.norm(p1 - p2)\n\nimport numpy as np\na, b, c = 3.33277, 3.78041, 10.06009\n\nprint(\"Testing some atom position configurations:\")\n\n# Test configuration: V at corners/cell, O and F interspersed\n# V atoms at low symmetry but non-overlapping positions\ntest_positions = [\n # V atoms\n ('V', 0.0, 0.0, 0.0),\n ('V', 0.2, 0.0, 0.0), \n ('V', 0.0, 0.2, 0.0),\n ('V', 0.2, 0.2, 0.2),\n]\n\n# O atoms around V atoms\nfor i, (sym, vx, vy, vz) in enumerate(test_positions):\n # Place O and F around this V at ~0.58 fractional unit (~1.9 Å)\n ox = vx + 0.15; oy = vy - 0.15; oz = vz + 0.15\n fx = vx - 0.15; fy = vy + 0.15; fz = vz - 0.15\n \n dist_ox = calc_dist([vx, vy, vz], [ox, oy, oz], a, b, c)\n dist_fx = calc_dist([vx, vy, vz], [fx, fy, fz], a, b, c)\n print(f\"V{i}: {vx:.2f},{vy:.2f},{vz:.2f} -> O: [{ox:.3f},{oy:.3f},{oz:.3f}]={dist_ox:.2f}Å, F: [{fx:.3f},{fy:.3f},{fz:.3f}]={dist_fx:.2f}Å\")\n\n# Better approach: create positions randomly but enforce minimum separation\n# Let me write a helper to find good sites\n\nprint(\"\\n\\nProposed structure:\")\nprint(\"=\"*60)\nprint(\"# need to properly relax - let me define coordinates more carefully\")\n\n# Define V0 at origin\n# V1 at (0.5, ~0.85, 0.85) like original but adjusted\n# V2 at (0.77, 0.74, 0.57) like original\n# V3 at (0.83, 0.55, 0.11) like original\n\n# These are from the original - let me use them as base but shift O and F\n\n# Original V positions (already reasonable):\norig_v = [\n (0.0, 0.0, 0.0), # approximating\n (0.26, 0.95, 0.85),\n (0.77, 0.74, 0.57),\n (0.83, 0.55, 0.11),\n]\n\n# Let me just write a decent CIF that passes validation\n# Key rule: atoms of same type shouldn't be too close, \n# 2.0 Å is minimum for first shell in VOF\nfinal_coords = [\n # V positions - fixed from original (already reasonable spacing)\n ('V', 0.0, 0.0, 0.0),\n ('V', 0.264, 0.950, 0.853),\n ('V', 0.771, 0.744, 0.566),\n ('V', 0.831, 0.548, 0.109),\n]\n\n# O positions - place at ~0.8-1.5 Å from V (tentative)\n# Adjusted from original to avoid issues\no_coords = [\n ('O', 0.1, 0.96, 0.57),\n ('O', 0.318, 0.82, 0.58),\n ('O', 0.76, 0.67, 0.43),\n ('O', 1.1, 0.10, 0.01), # this will wrap to ~0.1\n]\n\n# F positions\nf_coords = [\n ('F', 0.32, 0.68, 0.86),\n ('F', 0.0, 0.95, 0.28),\n ('F', 0.93, 0.26, 0.30),\n ('F', 0.55, 0.68, 0.69),\n]\n\n# Let me compute all distances and see if any are too short\ndef get_coord(lab, coords_list):\n for sym, x, y, z in coords_list:\n if sym == lab:\n return [x, y, z]\n raise ValueError(f\"No {lab} found\")\n\n# Update O7 to wrap properly\no_coords[3] = ('O', (1.20667685 % 1.0), 0.09962754, 0.01154999)\n\n# Let me recalculate distances better\ncoords = []\ncoords += [(lab, x, y, z) if isinstance(x, (int, float)) else (lab, x[0], x[1], x[2]) \n for lab, x, y, z in final_coords + [[x, (c%1) for c in get_coord(x, final_coords)] for x in o_coords] + [[x, (c%1) for c in get_coord(x, final_coords)] for x in f_coords]]\n\n# Actually let me just write a simple corrected CIF\n# The main issue seems to be the F-F and some V-Metal distances\n# Let me try to create a structure where atoms are well-separated\n\n# Simple approach: use ICSD or standard VOF structure if possible\n# Since I don't have reference, let me just ensure no atoms are too close\n\n# Using adjusted positions that should be valid\ngood_v_coords = [\n (\"V\", 0.01805, -0.01891, 0.36269), # wrap y to positive\n (\"V\", 0.26417, 0.95004, 0.85285),\n (\"V\", 0.77059, 0.74371, 0.56602),\n (\"V\", 0.83095, 0.54759, 0.10933),\n]\n\n# O coordinates - adjust some that may have overlaps\ngood_o_coords = [\n (\"O\", 0.16686, 0.67390, 0.43096),\n (\"O\", 0.31783, 0.70918, 0.99001),\n (\"O\", 0.76417, 0.85568, 0.43284),\n (\"O\", 0.20668, 0.09963, 0.01155), # wrapped from 1.20668\n]\n\n# F coordinates - need to fix F-F overlap at 1.20 Å\n# The issue was F8 at (0.32, 0.51, 0.76) and F11 at (0.55, 0.68, 0.69)\n# These are too close. Let me move F11\ngood_f_coords = [\n (\"F\", 0.32151, 0.50781, 0.75968),\n (\"F\", -0.00243, 0.77766, 0.27918),\n (\"F\", 0.92612, 0.25516, 0.30142),\n (\"F\", 0.55399, 0.43424, 0.73467), # moved from (0.55, 0.68, 0.69) to avoid F8\n]\n\n# Verify this structure\ndef dist_atmx(atoms, i, j, a, b, c):\n p1 = np.array([a*atoms[i][1], b*atoms[i][2], c*atoms[i][3]]) # atoms[i] is (lab, x, y, z)\n p2 = np.array([a*atoms[j][1], b*atoms[j][2], c*atoms[j][3]])\n return np.linalg.norm(p1 - p2)\n\na, b, c = 3.33277, 3.78041, 10.06009\natoms_list = [(c[0], c[1] % 1, c[2] % 1, c[3] % 1) for c in good_v_coords + good_o_coords + good_f_coords]\n\nclose_pairs = []\nfor i in range(len(atoms_list)):\n for j in range(i+1, len(atoms_list)):\n d = dist_atmx(atoms_list, i, j, a, b, c)\n if d < 2.0:\n print(f\"Too close {atoms_list[i][0]}-{atoms_list[j][0]}: {d:.3f} Å\")\n close_pairs.append((atoms_list[i][0], atoms_list[j][0], d))\n elif d < 2.5:\n print(f\"Short {atoms_list[i][0]}-{atoms_list[j][0]}: {d:.3f} Å\")\n\nif len(close_pairs) < 4:\n print(f\"\\nOK! Only {len(close_pairs)} pairs too close (at most 4 short bonds allowed)\")\nelse:\n print(f\"\\nPROBLEM: {len(close_pairs)} pairs too close!\")" } """ error: """ Cell In[6], line 164 for lab, x, y, z in final_coords + [[x, (c%1) for c in get_coord(x, final_coords)] for x in o_coords] + [[x, (c%1) for c in get_coord(x, final_coords)] for x in f_coords]] ^ SyntaxError: did you forget parentheses around the comprehension target?""" ## Step 0 tool-call audit Run `e7oej0cxoe3j6lwy0tz9euou` step `0` returned `64` parsed samples from `64` total samples. I counted `610` executed tool calls, with mean `9.53` and median `10.0` calls per rollout. Mean reward across parsed step-0 rollouts was `0.168`. Audit artifacts: `outputs/rlm_training_analysis/e7oej0cxoe3j6lwy0tz9euou/step_0/tool_calls.csv`, `outputs/rlm_training_analysis/e7oej0cxoe3j6lwy0tz9euou/step_0/rollout_summary.csv`, and `outputs/rlm_training_analysis/e7oej0cxoe3j6lwy0tz9euou/step_0/summary.md`. Primary-label counts: | label | count | | --- | --- | | candidate_generation | 58 | | checker_use | 52 | | finalization | 8 | | geometry_analysis | 87 | | harness_exploration | 120 | | input_inspection | 264 | | other | 21 | Error subtype counts: | error_subtype | count | | --- | --- | | cif_parse_error | 2 | | file_io_error | 1 | | import_error | 10 | | no_observable_signal | 1 | | runtime_code_error | 94 | | syntax_error | 23 | Python exception detail counts: | exception_detail | count | | --- | --- | | AttributeError | 33 | | ImportError | 6 | | IndexError | 11 | | KeyError | 4 | | ModuleNotFoundError | 4 | | NameError | 10 | | ParserError | 1 | | StructureError | 1 | | SyntaxError | 23 | | TypeError | 22 | | ValueError | 11 | | ZeroDivisionError | 1 | Error cause tag counts: | cause_tag | count | | --- | --- | | constructor_signature_misuse | 5 | | empty_collection_assumption | 1 | | immutable_tuple_mutation | 1 | | manual_cif_parse_shape_assumption | 10 | | numeric_parse_assumption | 1 | | pymatgen_api_expectation | 52 | | pymatgen_cifparser_api_expectation | 5 | | pymatgen_composition_property_expectation | 8 | | pymatgen_crystalnn_api_expectation | 9 | | pymatgen_neighbor_dict_key_expectation | 4 | | pymatgen_site_api_expectation | 9 | | pymatgen_spacegroup_api_expectation | 6 | | pymatgen_structure_api_expectation | 12 | | pymatgen_structure_validation_failure | 1 | | syntax_assignment_expression_misuse | 2 | | syntax_bad_comprehension | 1 | | syntax_bad_line_continuation | 1 | | syntax_fstring | 2 | | syntax_incomplete_try_block | 1 | | syntax_invalid_import_statement | 4 | | syntax_invalid_star_expression | 1 | | syntax_invalid_syntax | 1 | | syntax_missing_comma | 1 | | syntax_missing_multiplication_operator | 9 | | syntax_unmatched_delimiter | 3 | | syntax_unterminated_string | 1 | | tuple_unpacking_shape_assumption | 3 | | undefined_variable | 10 | | undefined_variable_cdist | 1 | | undefined_variable_cos | 1 | | undefined_variable_grades | 1 | | undefined_variable_idx | 1 | | undefined_variable_loop | 1 | | undefined_variable_pos | 2 | | undefined_variable_re | 1 | | undefined_variable_sqrt | 1 | | undefined_variable_struct | 1 | | wrong_operand_type | 7 | Silent/non-exception bug counts: | bug_tag | count | | --- | --- | | stateful_variable_reuse | 28 | | blank_print_output | 67 | | missing_marker_slice | 1 | | commented_out_code | 0 | | empty_parse_result | 2 | Exploration and analysis before acting: | metric | value | | --- | --- | | avg first candidate turn | 4.54 | | avg first checker turn | 4.19 | | harness reads before first candidate | 120 | | harness reads before first checker | 117 | | geometry calls before first checker | 38 | | checker calls | 52 | | candidate-generation calls | 58 | Most common secondary tags: | tag | count | | --- | --- | | reads_input | 226 | | structure_inspection | 218 | | reads_skill | 104 | | distance_calc | 104 | | pymatgen_attempt | 94 | | runtime_code_error | 94 | | manual_cif | 68 | | blank_print_output | 67 | | file_write | 56 | | checker_invocation | 52 | | pymatgen_api_expectation | 52 | | file_read | 28 | Reasoning/context tag counts: | reasoning_tag | count | | --- | --- | | checker_feedback_reasoning | 253 | | structure_reasoning | 231 | | distance_reasoning | 162 | | angle_reasoning | 114 | | pymatgen_reasoning | 26 | Behaviour examples: | behaviour | sample | turn | reward | label | error | intent | | --- | --- | --- | --- | --- | --- | --- | | harness exploration | 0 | 1 | 1.0 | harness_exploration | | I can see this is a GaBi (gallium nitride? No, gallium antimonide) structure with orthorhombic/monoclinic space group P 1. The lattice parameters show some unusual angles (beta ~135 degrees). Let me read the SKILL.md... | | input inspection | 0 | 0 | 1.0 | input_inspection | | Let me start by reading the unrelaxed CIF file to understand the structure I need to work with. | | geometry analysis | 2 | 7 | 0.1 | geometry_analysis | runtime_code_error | I see some atoms are quite close: - Pd-Pd: 2.279, 1.546 - these are a bit close for transition metals - Mg-Mg: 1.363, 1.905 - 1.363 is quite close - Mg-Pd: 1.530, 1.5298 - these are very close The lattice is helical/t... | | candidate generation | 0 | 3 | 1.0 | candidate_generation | | I notice the positions look somewhat scattered. Let me think about a simpler approach. Given the constraints, let me write a CIF file that: 1. Has P1 space group 2. Has more reasonable lattice parameters (near-orthogo... | | checker use | 0 | 6 | 1.0 | checker_use | | Now let me run the checker to see the feedback. | | syntax error | 3 | 5 | 0.1 | input_inspection | syntax_error | Now let me analyze interatomic distances to understand the bonding. I need to map each atom's fractional coordinates to cartesian coordinates and compute distances. | | import error | 5 | 4 | 0.1 | candidate_generation | import_error | The current structure has two main issues: 1. Bond lengths look unreasonable (bond_score=0.565, target >= 0.75) 2. Force proxy indicates possible atomic clashes (max_force_proxy=1.166, target < 0.1) The formation ener... | | runtime code error | 2 | 7 | 0.1 | geometry_analysis | runtime_code_error | I see some atoms are quite close: - Pd-Pd: 2.279, 1.546 - these are a bit close for transition metals - Mg-Mg: 1.363, 1.905 - 1.363 is quite close - Mg-Pd: 1.530, 1.5298 - these are very close The lattice is helical/t... | | CIF parse/checker failure | 7 | 7 | 0.1 | checker_use | cif_parse_error | Now let me run the checker to see what issues exist. | | no observable signal | 9 | 1 | 0.1 | input_inspection | no_observable_signal | Let me analyze this CIF file: 1. Formula: K3Ho3S6 (which is KHoS2 per unit) 2. Space group: P 1 (triclinic) 3. Cell parameters: a=4.035, b=4.035, c=22.09 with alpha=beta=90, gamma=120 4. Wait, that's a weird unit cell... | | stateful variable reuse | 1 | 3 | 1.0 | harness_exploration | stateful_variable_reuse | The main issue is that the force proxy indicates possible atomic clashes (max_force_proxy=0.516, target < 0.1). I have one more checker call available. The structure is LiTmAu2 in P1 space group. The problem is likely... | | blank print output | 0 | 9 | 1.0 | finalization | blank_print_output | The checker says "valid": True with no issues. The check budget is exhausted but I was told to copy the best current candidate to /task/final.cif and stop. The structure passed with: - valid: true - issues: [] (empty)... | | missing marker slice | 17 | 4 | 0.1 | input_inspection | stateful_variable_reuse\|blank_print_output\|missing_marker_slice | Let me look at the raw lines more carefully. | | empty parse result | 17 | 2 | 0.1 | input_inspection | stateful_variable_reuse\|empty_parse_result | Now I understand. Let me analyze the structure I'm given: 1. Formula: Tm2MgCd (from _chemical_formula_structural) - But _chemical_formula_sum says Tm8 Mg4 Cd4, which would be Tm8Mg4Cd4 or simplifying Tm2MgCd * 4 - Z =... | Per-rollout compact audit: | sample | reward | calls | first_candidate | first_checker | harness | input | geometry | candidate | checker | final | errors | error_subtypes | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | 1.000 | 10 | 3 | 6 | 1 | 2 | 0 | 4 | 2 | 1 | 0 | | | 1 | 1.000 | 8 | 4 | 2 | 2 | 1 | 0 | 1 | 2 | 1 | 0 | | | 2 | 0.100 | 10 | | | 3 | 2 | 3 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 3 | 0.100 | 9 | 7 | | 2 | 6 | 0 | 1 | 0 | 0 | 1 | syntax_error:1 | | 4 | 0.100 | 10 | 8 | 9 | 2 | 4 | 1 | 1 | 1 | 0 | 2 | runtime_code_error:1\|syntax_error:1 | | 5 | 0.100 | 10 | 2 | 3 | 1 | 1 | 1 | 6 | 1 | 0 | 5 | import_error:2\|runtime_code_error:2\|syntax_error:1 | | 6 | 0.100 | 10 | | | 3 | 6 | 0 | 0 | 0 | 0 | 4 | runtime_code_error:4 | | 7 | 0.100 | 10 | 6 | 7 | 2 | 4 | 0 | 2 | 2 | 0 | 2 | cif_parse_error:1\|runtime_code_error:1 | | 8 | 0.100 | 7 | | | 2 | 1 | 4 | 0 | 0 | 0 | 2 | runtime_code_error:1\|syntax_error:1 | | 9 | 0.100 | 10 | | 2 | 0 | 9 | 0 | 0 | 1 | 0 | 6 | no_observable_signal:1\|runtime_code_error:5 | | 10 | 0.100 | 10 | | 2 | 1 | 7 | 1 | 0 | 1 | 0 | 4 | runtime_code_error:1\|syntax_error:3 | | 11 | 1.000 | 10 | 4 | 5 | 2 | 1 | 2 | 2 | 2 | 1 | 0 | | | 12 | 0.100 | 10 | 7 | 4 | 2 | 4 | 0 | 2 | 1 | 0 | 2 | runtime_code_error:2 | | 13 | 0.100 | 10 | 3 | 4 | 2 | 4 | 1 | 2 | 1 | 0 | 3 | runtime_code_error:3 | | 14 | 0.100 | 7 | | 5 | 2 | 4 | 0 | 0 | 1 | 0 | 1 | runtime_code_error:1 | | 15 | 0.100 | 2 | | | 1 | 1 | 0 | 0 | 0 | 0 | 0 | | | 16 | 0.100 | 10 | | 3 | 2 | 1 | 6 | 0 | 1 | 0 | 5 | runtime_code_error:5 | | 17 | 0.100 | 10 | | | 1 | 7 | 1 | 0 | 0 | 0 | 0 | | | 18 | 0.100 | 9 | | | 2 | 7 | 0 | 0 | 0 | 0 | 3 | runtime_code_error:3 | | 19 | 0.100 | 10 | | | 2 | 4 | 4 | 0 | 0 | 0 | 2 | runtime_code_error:1\|syntax_error:1 | | 20 | 0.100 | 10 | 4 | 5 | 2 | 4 | 1 | 1 | 1 | 0 | 2 | runtime_code_error:2 | | 21 | 0.100 | 10 | 3 | 4 | 2 | 6 | 0 | 1 | 1 | 0 | 2 | runtime_code_error:2 | | 22 | 0.100 | 10 | | | 3 | 6 | 0 | 0 | 0 | 0 | 4 | runtime_code_error:3\|syntax_error:1 | | 23 | 0.100 | 10 | | 3 | 1 | 7 | 0 | 0 | 1 | 0 | 3 | runtime_code_error:1\|syntax_error:2 | | 24 | 0.100 | 10 | | | 2 | 6 | 0 | 0 | 0 | 0 | 1 | syntax_error:1 | | 25 | 0.100 | 10 | 4 | 6 | 2 | 2 | 0 | 5 | 1 | 0 | 3 | cif_parse_error:1\|import_error:1\|syntax_error:1 | | 26 | 0.100 | 10 | | 4 | 3 | 2 | 4 | 0 | 1 | 0 | 4 | import_error:1\|runtime_code_error:3 | | 27 | 0.100 | 10 | | 3 | 2 | 2 | 5 | 0 | 1 | 0 | 4 | runtime_code_error:4 | | 28 | 0.100 | 10 | | 3 | 2 | 6 | 1 | 0 | 1 | 0 | 5 | import_error:1\|runtime_code_error:3\|syntax_error:1 | | 29 | 0.100 | 10 | | | 4 | 6 | 0 | 0 | 0 | 0 | 3 | runtime_code_error:3 | | 30 | 0.100 | 10 | 2 | 3 | 1 | 1 | 6 | 1 | 1 | 0 | 0 | | | 31 | 0.100 | 10 | 9 | 3 | 2 | 6 | 0 | 1 | 1 | 0 | 1 | runtime_code_error:1 | | 32 | 0.400 | 10 | 3 | 6 | 1 | 3 | 0 | 3 | 2 | 1 | 0 | | | 33 | 0.100 | 9 | 2 | 3 | 1 | 1 | 5 | 1 | 1 | 0 | 2 | runtime_code_error:2 | | 34 | 0.100 | 10 | 7 | 8 | 1 | 5 | 1 | 1 | 1 | 0 | 0 | | | 35 | 0.250 | 10 | 5 | 2 | 1 | 6 | 0 | 1 | 2 | 0 | 0 | | | 36 | 0.250 | 10 | 3 | 4 | 2 | 1 | 0 | 3 | 2 | 2 | 0 | | | 37 | 0.100 | 9 | | 3 | 2 | 4 | 2 | 0 | 1 | 0 | 1 | runtime_code_error:1 | | 38 | 0.100 | 10 | | | 3 | 5 | 2 | 0 | 0 | 0 | 3 | runtime_code_error:3 | | 39 | 0.100 | 10 | 5 | 8 | 2 | 1 | 3 | 3 | 1 | 0 | 1 | runtime_code_error:1 | | 40 | 0.100 | 7 | | | 2 | 1 | 4 | 0 | 0 | 0 | 1 | runtime_code_error:1 | | 41 | 0.100 | 9 | | | 2 | 7 | 0 | 0 | 0 | 0 | 2 | syntax_error:2 | | 42 | 0.100 | 10 | | 2 | 0 | 5 | 3 | 0 | 1 | 0 | 4 | import_error:1\|runtime_code_error:3 | | 43 | 0.100 | 10 | | | 2 | 7 | 1 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 44 | 0.100 | 10 | 8 | 3 | 2 | 2 | 2 | 2 | 2 | 0 | 2 | runtime_code_error:2 | | 45 | 0.100 | 9 | 5 | 7 | 4 | 1 | 0 | 3 | 1 | 0 | 0 | | | 46 | 0.100 | 10 | | | 3 | 2 | 4 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 47 | 0.100 | 10 | | 5 | 2 | 7 | 0 | 0 | 1 | 0 | 2 | runtime_code_error:1\|syntax_error:1 | | 48 | 0.100 | 10 | | 3 | 2 | 6 | 1 | 0 | 1 | 0 | 4 | import_error:1\|runtime_code_error:2\|syntax_error:1 | | 49 | 0.100 | 10 | | | 2 | 7 | 0 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 50 | 0.100 | 8 | | 7 | 1 | 2 | 4 | 0 | 1 | 0 | 4 | import_error:1\|runtime_code_error:3 | | 51 | 0.100 | 10 | 4 | 3 | 2 | 1 | 2 | 4 | 1 | 0 | 3 | import_error:2\|runtime_code_error:1 | | 52 | 0.100 | 9 | 5 | 4 | 1 | 5 | 0 | 2 | 1 | 0 | 2 | runtime_code_error:2 | | 53 | 1.000 | 8 | 3 | 4 | 2 | 1 | 0 | 2 | 2 | 1 | 0 | | | 54 | 0.100 | 10 | | | 2 | 7 | 0 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 55 | 0.100 | 10 | | | 2 | 4 | 4 | 0 | 0 | 0 | 1 | syntax_error:1 | | 56 | 0.250 | 10 | 2 | 3 | 1 | 1 | 0 | 3 | 2 | 1 | 0 | | | 57 | 0.100 | 10 | | 2 | 1 | 5 | 3 | 0 | 1 | 0 | 1 | runtime_code_error:1 | | 58 | 0.100 | 10 | | | 3 | 6 | 0 | 0 | 0 | 0 | 4 | file_io_error:1\|runtime_code_error:3 | | 59 | 0.100 | 10 | | | 2 | 4 | 3 | 0 | 0 | 0 | 2 | runtime_code_error:2 | | 60 | 0.100 | 10 | | 7 | 2 | 7 | 0 | 0 | 1 | 0 | 1 | runtime_code_error:1 | | 61 | 0.100 | 10 | | 3 | 2 | 7 | 0 | 0 | 1 | 0 | 4 | runtime_code_error:1\|syntax_error:3 | | 62 | 0.100 | 10 | | 3 | 2 | 5 | 2 | 0 | 1 | 0 | 1 | syntax_error:1 | | 63 | 0.100 | 10 | | | 2 | 8 | 0 | 0 | 0 | 0 | 2 | runtime_code_error:2 |